SAFETY FIRST FOR AUTOMATED DRIVING
https://www.daimler.com/documents/innovation/other/safety-first-for-automated-driving.pdf

の、大事かもしれない点、追加した方がよいかもしれない点、削除した方がよいかもしれない点を歴してみる。

<この項は書きかけです。順次追記します。>

追加1. L5を含むとよい

ABSTRUCT
This publication summarizes widely known safety by design and verification and validation (V&V) methods of SAE L3 and L4 automated driving.

L5を対象にしないのは残念だ。
合意が難しいからこそ、候補を列記するだけでも有用だと思っている。

追加w. Safety Guideを含むとよい。

REFERENCED STANDARDS
ISO/PAS 21448:2019
ISO 26262:2018
ISO/SAE CD 21434
ISO 19157:2013
ISO/TS 19158:2012
ISO/TS 16949:2009
ISO/IEC 2382-1:1993
ISO/IEC/IEEE 15288:2015
Road Vehicles – Safety of the intended functionality (SOTIF) Road Vehicles – Functional safety
Road Vehicles – Cybersecurity engineering
Geographic information – Data quality
Geographic information – Quality assurance of data supply
Quality management systems – Particular requirements for the application of ISO 9001:2008 for automotive production and relevant service part organizations Information technology – Vocabulary – Part 1: Fundamental terms
Systems and software engineering – System life cycle processes

ISO/IEC Guide 50
ISO/IEC Guide 51
を含むとよい。
機械安全、電気安全も含むとよい。

追加3. HAZOP, ETA, FTA

List of Abbreviations
ADAS
ADS
ASIL
AUTO-ISAC
AUTOSAR
CERTS
CPU
CPP
CRC
DDT
DESTATIS
DFMEA
DiL
DNN
E/E
ECU
EPS
EU
FMEA
FMVSS
FUSA
GDPR
GNSS
GPS
GPU
HiL
HMI
HW
HW REPRO.
HWP
I/O Port
IEC
IEEE
IMU
IPsec
ISO
ISTQB
LIDAR
MCU
MRC
Advanced Driver Assistance System
Automated Driving System
Automotive Safety Integrity Level
Automotive Information Sharing and Analysis Center AUTOmotive Open System Architecture
Computer Emergency Response Team Central Processing Unit
Car Park Pilot
Cyclic Redundancy Check
Dynamic Driving Task
(Statistisches Bundesamt) Federal Statistical Office of Germany Design Failure Mode and Effect Analysis
Driver-in-the-Loop
Deep Neural Network
Electrical/Electronic
Electronic Control Unit
Electric Power Steering
European Union
Failure Mode and Effects Analysis
Federal Motor Vehicle Safety Standards
Functional Safety
European General Data Protection Regulation
Global Navigation Satellite System
Global Positioning System
Graphics Processing Unit
Hardware-in-the-Closed-Loop
Human-Machine Interaction
Hardware
Hardware Reprocessing
Highway Pilot
Input/Output Port
International Electrotechnical Commission
Institute of Electrical and Electronics Engineers
Inertial Measurement Unit
Internet Protocol Security
International Organization for Standardization
International Software Testing Qualifications Board
Light Detection and Ranging
Microcontroller Unit
Minimal Risk Condition
VIII
MRM NDS NHTSA NTSB ODD OEM OR OTP OUT PG RAMSS RMA SDL SiL SoC SOTIF STVG SW
SW REPRO. TJP
TLS
UNECE
UP V&V
Minimal risk maneuver
Naturalistic Driving Study
National Highway Traffic Safety Administration
National Transportation Safety Board
Operational Design Domain
Original Equipment Manufacturer
Open Road
One True Pairing
Object Under Test
Proving Ground
Reliability, Availability, Maintainability, Safety and Security Reliable Map Attribute
Secure Development Lifecycle Simulation-in-the-Closed-Loop
System on Chip
Safety of the Intended Functionality (Straßenverkehrsgesetz) German Road Traffic Act Software
Software Reprocessing
Traffic Jam Pilot
Transport Layer Security
United Nations Economic Commission for Europe
Urban Pilot
Verification and Validation

追加 4 参考文献の参考文献をつける。

参考文献に番号をつけ、参考文献の参考文献を項目として記す。

8 References
[1] ABI & THATCHAM RESEARCH. (2017). Regulating Automated Driving – The UK Insurer View. UK.
[2] ARNOLD, E., AL-JARRAH, O. Y., DIANATI, M., FALLAH, S., OXTOBY, D., & MOUZAKITIS, A. (2019). A Survey on 3D Object Detection Methods for Autonomous Driving Applications. IEEE Transactions on Intelligent Transportation Systems, 1-14. doi:10.1109/TITS.2019.2892405
https://ieeexplore.ieee.org/document/8621614
[3] BAINBRIDGE, L. (1983). Ironies of Automation. In Analysis, Design and Evaluation of Man–Machine Systems. Proceedings of the IFAC/IFIP/IFORS/IEA Conference, Baden-Baden, Federal Republic of Germany, 27–29 September 1982 (pp. 129-135). UK: Pergamon.
[4] BANSAL, M., KRIZHEVSKY, A., & OGALE, A. (2018). ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst. Waymo Research. Retrieved from https://arxiv.org/pdf/1812.03079.pdf
[5] BOJARSKI, M., DEL TESTA, D., DWORAKOWSKI, D., FIRNER, B., FLEPP, B., GOYAL, P., & ZIEBA, K. (2016). End to End Learning for Self-Driving Cars. arXiv:1604.07316. Retrieved from https://arxiv.org/pdf/1604.07316.pdf
[6] CALIFORNIA VEHICLE CODE 22350 VC. (1959). Division 11: Rules of the Road. Chapter 7: Speed Laws. California. Retrieved from https://leginfo.legislature.ca.gov/faces/codes_displaySection.xhtml?lawCode=VEH&sectionNum=22350
[7] CENSI, A., SLUTSKY, K., WONGPIROMSARN, T., YERSHOV, D. S., PENDLETON, S., FU, J., & FRAZZOLI, E. (2019). Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks. CoRR, abs/1902.09355. Retrieved from https://arxiv.org/pdf/1902.09355.pdf
[8] ECE/TRANS/WP.1/165. (2018). Report of the Global Forum for Road Traffic Safety on its Seventy- Seventh Session – Annex 1. Geneva. Retrieved from http://www.unece.org/fileadmin/DAM/trans/doc/2018/wp1/ECE-TRANS-WP1-165e.pdf
[9] ECLIPSE FOUNDATION. (2019). OpenPASS Working Group. Retrieved from www.openpass.eclipse. org: https://openpass.eclipse.org/
[10] ESSER, P., SUTTER, E., & OMMER, B. (2018). A Variational U-Net for Conditional Appearance and Shape Generation. arXiv:1804.04694. Retrieved from https://arxiv.org/pdf/1804.04694.pdf
[11] EUCAR. (2018). Self-Driving Vehicles: European Automotive R&D Leading the Global Race. Retrieved from https://www.eucar.be/self-driving-vehicles-european-automotive-rd-leading-the-global-race/
http://documents.epo.org/projects/babylon/eponet.nsf/0/65910DF6D3F02057C125833C004DB1E6/$File/self_driving_vehicles_study_en.pdf
[12] FAHRENKROG, F. (2016). Wirksamkeitsnalanyse von Fahrerassistenzsystemen in Bezug auf die Verkehrssicherheit. RWTH Aachen University. Aachen: fka GmbH.
[13] FEDERAL MINISTER OF TRANSPORT AND DIGITAL INFRASTRUCTURE (BMVI). (June, 2017). Ethics Commission – Automated and Connected Driving. Berlin. Retrieved from https://www.bmvi.de/SharedDocs/EN/publications/report-ethics-commission-automated-and-connected-driving.pdf?__ blob=publicationFile
[14] FELDHÜTTER, A., SEGLER, C., & BENGLER, K. (2018). Does Shifting Between Conditionally and Partially Automated Driving Lead to a Loss of Mode Awareness? In N. Stanton (Ed.), Advances in Human Aspects of Transportation. AHFE 2017. Advances in Intelligent Systems and Computing (597, pp. 730-741). Springer, Cham. doi:https://doi.org/10.1007/978-3-319-60441-1_70
[15] FORM, T. (2018). PEGASUS Method for Assessment of Highly Automated Driving Function. SIP-Adus Workshop 2018, 13-15 November, 2018. Tokyo International Exchange Center, Tokyo, Japan. Retrieved from http://en.sip-adus.go.jp/evt/workshop2018/file/PEGASUS_SIP-adus_Thomas_Form.pdf
[16] FRAADE-BLANAR, L., BLUMENTHAL, M., ANDERSON, J., & KALRA, N. (2018). Measuring Automated Vehicle Safety – Forging a Framework. Santa Monica, CA: RAND Corporation. Retrieved from https://www.rand.org/pubs/research_reports/RR2662.html
[17] GAL, Y. (2016). Uncertainty in Deep Learning. Dissertation, University of Cambridge, UK. Retrieved from http://mlg.eng.cam.ac.uk/yarin/thesis/thesis.pdf
[18]GESAMTVERBAND DER DEUTSCHEN VERSICHERUNGSWIRTSCHAFT E.V. (2018). Technische Aspekte des automatisierten Fahrens und Verkehrssicherheit. (84). Germany. Retrieved from https://udv.de/download/file/fid/11115
[19] GOLD, C. G. (2016). Modeling of Take-Over Performance in Highly Automated Vehicle Guidance. Dissertation, Technische Universität München. Retrieved from https://mediatum.ub.tum.de/doc/1296132/document.pdf
[20] GOLD, C., DAMBÖCK, D., LORENZ, L., & BENGLER, K. (2013). “Take Over!” How Long Does It Take To Get The Driver Back Into The Loop? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 1938-1942. doi:https://doi.org/10.1177/1541931213571433
[21] HANKE, T., HIRSENKORN, N., VAN-DRIESTEN, C., GARCIA-RAMOS, P., SCHIEMENTZ, M., SCHNEIDER, S., & BIEBL, E. (2017). Open Simulation Interface: A Generic Interface for the Environment Perception of Automated Driving Functions in Virtual Scenarios. Retrieved from www.github.com: http://www.hot.ei.tum.de/forschung/automotive-veroeffentlichungen
[21]HERE. (2019). HERE Hazard Warnings – Keeping Vehicles, and Their Drivers, Perfectly Alert. Retrieved June 18th, 2019, from www.here.com: https://www.here.com/products/automotive/hazard-warnings
[22] HINTON, J., & SEJNOWSKI, T. (1999). Unsupervised Learning: Foundations of Neural Computation. Cambridge, MA: MIT Press.
[23] IEC 61508. (2010). 2nd Ed. Functional Safety of Electrical/Electronic/Programmable Electronic Safety- Related Systems (all parts).
[24] INFORMAL WORKING GROUP ON INTELLIGENT TRANSPORT SYSTEMS/AUTOMATED DRIVING (IWG). (2017). Proposal for the Definitions of Automated Driving under WP.29 and the General Principles for Developing a UN Regulation on Automated Vehicles. Geneva: UNECE.
[25] ISO 26262. (2018). Road Vehicles – Functional Safety.
[26] ISO/IEC 15288. (2015). Systems and Software Engineering – System Life Cycle Processes.
[27] ISO/PAS 21448. (2018). Road Vehicles – Safety of The Intended Functionality.
[28] ISO/SAE CD 21434. (Under Development). Road Vehicles – Cybersecurity Engineering.
[29] KALRA, N., & PADDOCK, S. (2016). Driving to Safety – How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? Santa Monica, CA: RAND Corporation. Retrieved from https://www.rand.org/pubs/research_reports/RR1478.html
[30] KERSCHBAUM, P., LORENZ, L., & BENGLER, K. (2014). Highly Automated Driving with a Decoupled Steering Wheel. Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting, 58(1), 1686-1690. Los Angeles, CA.
KNAPP, A., NEUMANN, M., BROCKMANN, M., WALZ, R., & WINKLE, T. (2009). PReVENT RESPONSE III: Code of Practice for the Design and Evaluation of ADAS. Retrieved from https://www. acea.be/uploads/publications/20090831_Code_of_Practice_ADAS.pdf
KOOPMAN, P., & WAGNER, M. (2018). Toward a Framework for Highly Automated Vehicle Safety Validation. SAE Technical Paper 2018-01-1071. doi:https://doi.org/10.4271/2018-01-1071
LORENZ, L., KERSCHBAUM, P., & SCHUHMANN, J. (2014). Designing Take Over Scenarios for Automated Driving: How Does Augmented Reality Support the Driver to get Back into the Loop? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 1681-1685. doi:https://doi.org/10.1177/1541931214581351
MENZEL, T., BAGSCHIK, G., & MAURER, M. (2018). Scenarios for Development, Test and Validation of Automated Vehicles. In 2018 IEEE Intelligent Vehicles Symposium IV 2018, Changshu, Suzhou, China, June 26-30, 2018. Retrieved from https://arxiv.org/pdf/1801.08598.pdf
MICROSOFT. (2019). Microsoft Security Development Lifecycle (SDL). Retrieved from www.microsoft.com: https://www.microsoft.com/en-us/securityengineering/sdl/
NATIONAL HIGHWAY TRANSPORTATION SAFETY ADMINISTRATION (NHTSA). (2017). Automated Driving Systems 2.0 – A Vision for Safety. Retrieved from https://www.nhtsa.gov/sites/ nhtsa.dot.gov/files/documents/13069a-ads2.0_090617_v9a_tag.pdf
NATIONAL TRANSPORTATION SAFETY BOARD (NTSB). (2017). Collision Between a Car Operating With Automated Vehicle Control Systems and a Tractor-Semitrailer Truck Near Williston, Florida, May 7, 2016. NTSB/HAR-17/02. Retrieved from https://www.ntsb.gov/investigations/AccidentReports/ Reports/HAR1702.pdf
NHTSA‘S NATIONAL CENTER FOR STATISTICS AND ANALYSIS (NCSA). (February, 2015). TRAFFIC SAFETY FACTS. Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. USA.
NPC. (2017). Surveying and Mapping Law of the People‘s Republic of China. Retrieved from http://www. npc.gov.cn/englishnpc/Law/2007-12/12/content_1383865.htm
OHNO, T. (1988). Toyota Production System: Beyond Large-Scale Production. Portland, Oregon: Productivity Press.
OpenDRIVE. (2018). OpenDRIVE Standard. Retrieved from http://www.opendrive.org/project.html OpenSCENARIO. (2017). OpenSCENARIO Specification Rev. 0.9.1. Retrieved from http://www.
openscenario.org/download.html
PEGASUS. (2019). PEGASUS Method – An Overview. Retrieved from https://www.pegasusprojekt.de/ files/tmpl/Pegasus-Abschlussveranstaltung/PEGASUS-Gesamtmethode.pdf
PETERMANN, I., & SCHLAG, B. (2010). Auswirkungen der Synthese von Assistenz und Automation auf das Fahrer-Fahrzeug System. 11. Symposium AAET 2010 – Automatisierungs-, Assistenzsysteme und eingebettete Systeme für Transportmittel, 257-266. Germany.
SAE J3016. (2018). Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles. Document J3016_201806. SAE International. doi:https://doi.org/10.4271/ J3016_201806.
SALAY, R., & CZARNECKI, K. (2018). Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262. Waterloo Intelligent Systems Engineering (WISE) Lab. University of Waterloo, Canada. Retrieved from https://arxiv.org/ftp/ arxiv/papers/1808/1808.01614.pdf
SCHMIDHUBER, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85–117.
SHALEV-SCHWARTZ, S., SHAMMAH, S., & SHASHUA, A. (2018). On a Formal Model of Safe and Scalable Self-driving Cars. CoRR, abs/1708.06374. Retrieved from http://arxiv.org/abs/1708.06374
STATISTISCHES BUNDESAMT (DESTATIS). (2018). Fachserie 8, Reihe 7, Verkehr, Verkehrsunfälle 2017. Germany.
STRASSENVERKEHRSGESETZ (STVG). (2018). §1a Kraftfahrzeuge mit hoch- oder vollautomatisierter Fahrfunktion. BGBl. I [in English: German Road Traffic Act, §1a Motor Vehicles with Highly or Fully Automated Driving Functions (Federal Law Gazette I)], 310,919. Germany.
SUTTON, R., & BARTO, A. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
TAGUE, N. (2005). The Quality Toolbox (2nd ed.). Wisconsin: Quality Press.
U.S. DEPARTMENT OF TRANSPORTATION (U.S. DOT). (2016). Federal Automated Vehicles Policy – September 2016. Retrieved from https://www.transportation.gov/AV/federal-automated- vehicles-policy-september-2016
U.S. DEPARTMENT OF TRANSPORTATION (U.S. DOT). (2018). Preparing for the Future of Transportation; Automated Vehicles 3.0. USA. Retrieved from https://www.transportation.gov/sites/ dot.gov/files/docs/policy-initiatives/automated-vehicles/320711/preparing-future-transportation- automated-vehicle-30.pdf
UNITED NATIONS. (1969). Vienna Convention on the Law of Treaties. 1155, 331. Retrieved from https:// www.refworld.org/docid/3ae6b3a10.html
VAN DEN BEUKEL, A. P., VAN DER VOORT, M. C., & EDGER, A. O. (2016). Supporting the Changing Driver’s Task: Exploration of Interface Designs for Supervision and Intervention in Automated Driving. Transportation Research Part F: Traffic Psychology and Behaviour, 43, 279-301. doi:https://doi. org/10.1016/j.trf.2016.09.009
WACHENFELD, W. (2017). How Stochastic can Help to Introduce Automated Driving. Dissertation, Technische Universität Darmstadt. Retrieved from http://tuprints.ulb.tu-darmstadt.de/5949/7/Diss_ Wf_2017_02_04_Ver%C3%B6ffentlichung.pdf
WINNER, H., HAKULI, H., LOTZ, F., & SINGER, C. (EDS.). (2016). Handbook of Driver Assistance Systems (1 ed.). Switzerland: Springer International Publishing. doi:10.1007/978-3-319-12352-3
ZEEB, K., BUCHNER, A., & SCHRAUF, M. (2016). Is Take-Over Time All That Matters? The Impact of Visual-Cognitive Load on Driver Take-Over Quality after Conditionally Automated Driving. Accident Analysis & Prevention, 92, 230-239. https://doi.org/10.1016/j.aap.2016.04.002

参考文献の参考文献

[4] References

Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller. Explaining how a deep neural network trained with end-to-end learning steers a car. arXiv preprint arXiv:1704.07911, 2017.
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE International Conference on Computer Vision, pages 2722–2730, 2015.
Felipe Codevilla, Matthias Mu ̈ller, Antonio L ́opez, Vladlen Koltun, and Alexey Dosovit- skiy. End-to-end driving via conditional imitation learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1–9. IEEE, 2018.
Nemanja Djuric, Vladan Radosavljevic, Henggang Cui, Thi Nguyen, Fang-Chieh Chou, Tsung-Han Lin, and Jeff Schneider. Motion prediction of traffic actors for autonomous driving using deep convolutional networks. arXiv preprint arXiv:1808.05819, 2018.
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. arXiv preprint arXiv:1711.03938, 2017.
Nathaniel Fairfield and Chris Urmson. Traffic light mapping and detection. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 5421–5426. IEEE, 2011.
Simon Hecker, Dengxin Dai, and Luc Van Gool. Learning driving models with a surround- view camera system and a route planner. arXiv preprint arXiv:1803.10158, 2018.
Alex Kuefler, Jeremy Morton, Tim Wheeler, and Mykel Kochenderfer. Imitating driver behavior with generative adversarial networks. In Intelligent Vehicles Symposium (IV), 2017 IEEE, pages 204–211. IEEE, 2017.
Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, and Ken Goldberg. Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations. In Robotics and Automation (ICRA), 2017 IEEE International Conference on, pages 358–365. IEEE, 2017a.
Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, and Ken Goldberg. Dart: Noise injection for robust imitation learning. arXiv preprint arXiv:1703.09327, 2017b.
Donghan Lee, Youngwook Paul Kwon, Sara McMains, and J Karl Hedrick. Convolution neural network-based lane change intention prediction of surrounding vehicles for acc. In Intelligent Transportation Systems (ITSC), 2017 IEEE 20th International Conference on, pages 1–6. IEEE, 2017.
Bansal, Krizhevsky & Ogale
Wenjie Luo, Bin Yang, and Raquel Urtasun. Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018.
Matthias Mu ̈ller, Alexey Dosovitskiy, Bernard Ghanem, and Vladen Koltun. Driving policy transfer via modularity and abstraction. arXiv preprint arXiv:1804.09364, 2018.
Brian Paden, Michal Cˇ ́ap, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans- actions on intelligent vehicles, 1(1):33–55, 2016.
Xinlei Pan, Yurong You, Ziyan Wang, and Cewu Lu. Virtual to real reinforcement learning for autonomous driving. arXiv preprint arXiv:1704.03952, 2017.
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. Deepxplore: Automated whitebox testing of deep learning systems. In Proceedings of the 26th Symposium on Operating Systems Principles, pages 1–18. ACM, 2017.
Dean A Pomerleau. Alvinn: An autonomous land vehicle in a neural network. In Advances in neural information processing systems, pages 305–313, 1989.
St ́ephane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 627–635, 2011.
Axel Sauer, Nikolay Savinov, and Andreas Geiger. Conditional affordance learning for driving in urban environments. arXiv preprint arXiv:1806.06498, 2018.
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua. Safe, multi-agent, reinforce- ment learning for autonomous driving. arXiv preprint arXiv:1610.03295, 2016.
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th International Conference on Software Engineering, pages 303–314. ACM, 2018.
Huazhe Xu, Yang Gao, Fisher Yu, and Trevor Darrell. End-to-end learning of driving models from large-scale video datasets. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2174–2182, 2017.
Bin Yang, Ming Liang, and Raquel Urtasun. Hdnet: Exploiting hd maps for 3d object detection. In Conference on Robot Learning, pages 146–155, 2018.

[5] Reference

[1] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Backprop- agation applied to handwritten zip code recognition. Neural Computation, 1(4):541–551, Winter 1989. URL: http://yann.lecun.org/exdb/publis/pdf/lecun-89e.pdf.
[2] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012. URL: http://papers.nips.cc/paper/ 4824-imagenet-classification-with-deep-convolutional-neural-networks. pdf.
[3] L. D. Jackel, D. Sharman, Stenard C. E., Strom B. I., , and D Zuckert. Optical character recognition for self-service banking. AT&T Technical Journal, 74(1):16–24, 1995.
[4] Large scale visual recognition challenge (ILSVRC). URL: http://www.image-net.org/ challenges/LSVRC/.
[5] Net-Scale Technologies, Inc. Autonomous off-road vehicle control using end-to-end learning, July 2004. Final technical report. URL: http://net-scale.com/doc/net-scale-dave-report.pdf.
[6] Dean A. Pomerleau. ALVINN, an autonomous land vehicle in a neural network. Technical report, Carnegie Mellon University, 1989. URL: http://repository.cmu.edu/cgi/viewcontent. cgi?article=2874&context=compsci.
[7] Wikipedia.org. DARPA LAGR program. http://en.wikipedia.org/wiki/DARPA_LAGR_ Program.
[8] Danwei Wang and Feng Qi. Trajectory planning for a four-wheel-steering vehicle. In Proceedings of the 2001 IEEE International Conference on Robotics & Automation, May 21–26 2001. URL: http: //www.ntu.edu.sg/home/edwwang/confpapers/wdwicar01.pdf.
[9] DAVE 2 driving a lincoln. URL: https://drive.google.com/open?id= 0B9raQzOpizn1TkRIa241ZnBEcjQ.

[7] Reference

R. C. Arkin, “Governing Lethal Behavior: Embedding Ethics in a Hybrid Deliberative/Reactive Robot Architecture Part I: Motivation And Philosophy,” Proceedings of the 3rd international conference on Human robot interaction - HRI ’08, p. 121, jan 2008. [Online]. Available: http://portal.acm.org/citation.cfm?doid=1349822.1349839 S. M. Thornton, S. Pan, S. M. Erlien, and J. C. Gerdes, “Incorpo- rating ethical considerations into automated vehicle control,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1429–1439, June 2017.
E. Pires Bjørgen, S. Øvervatn Madsen, T. Skaar Bjørknes, F. Von- heim Heimsæter, R. Håvik, M. Linderud, P. Longberg, L. Dennis, and M. Slavkovik, “Cake, death, and trolleys: dilemmas as benchmarks of ethical decision-making,” in AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, New Orleans, USA, 2018, forthcoming. P. Foot, “The problem of abortion and the doctrine of double effect,” Oxford Review, vol. 5, pp. 5–15, 1967.
J. J. Thomson, “The trolley problem,” vol. 94, no. 6, pp. 1395–1415, 1985. [Online]. Available: http://www.jstor.org/stable/796133
“Moral machine (online),” 2016, http://moralmachine.mit.edu.
G. Fainekos, H. Kress-Gazit, and G. Pappas, “Temporal logic motion planning for mobile robots,” in Proc. of IEEE International Conference on Robotics and Automation, April 2005, pp. 2020–2025.
M. Kloetzer and C. Belta, “A fully automated framework for control of linear systems from temporal logic specifications,” IEEE Transactions on Automatic Control, vol. 53, no. 1, pp. 287–297, 2008.
P. Tabuada and G. J. Pappas, “Linear time logic control of linear systems,” IEEE Transaction on Automatic Control, vol. 51, no. 12, pp. 1862–1877, 2006. S.Karaman,R.G.Sanfelice,andE.Frazzoli,“Optimalcontrolofmixed logical dynamical systems with linear temporal logic specifications,” Dec. 2008, pp. 2117–2122.
J. Liu, N. Ozay, U. Topcu, and R. M. Murray, “Synthesis of reac- tive switching protocols from temporal logic specifications,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1771–1785, 2013.
M. Webster, M. Fisher, N. Cameron, and M. Jump, “Formal methods for the certification of autonomous unmanned aircraft systems,” in Proceedings of the 30th International Conference on Computer Safety, Reliability, and Security, ser. SAFECOMP’11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 228–242. [Online]. Available: http://dl.acm.org/citation.cfm?id=2041619.2041644
J. Morse, D. Araiza-Illan, K. Eder, J. Lawry, and A. Richards, “A fuzzy approach to qualification in design exploration for autonomous robots and systems,” in 2017 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2017, Naples, Italy, July 9-12, 2017, 2017, pp. 1–6. I.CizeljandC.Belta,“Negotiatingtheprobabilisticsatisfactionoftem- poral logic motion specifications,” in Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013.
H. Kress-Gazit, M. Lahijanian, and V. Raman, “Synthesis for robots: Guarantees and feedback for robot behavior,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 1, no. 1, pp. 211–236, 2018. [Online]. Available: https://doi.org/10.1146/ annurev- control- 060117- 104838
J. Van Den Hoven and G.-J. Lokhorst, “Deontic logic and computer- supported computer ethics,” Metaphilosophy, vol. 33, no. 3, pp. 376–386. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/ 10.1111/1467- 9973.00233
S. Levine and V. Koltun, “Continuous inverse optimal control with locally optimal examples,” in ICML ’12: Proceedings of the 29th International Conference on Machine Learning, 2012.
L. I. R. Castro, P. Chaudhari, J. Tumova, S. Karaman, E. Frazzoli, and D. Rus, “Incremental sampling-based algorithm for minimum-violation motion planning,” in 52nd IEEE Conference on Decision and Control, Dec 2013, pp. 3217–3224.
J. Tumova, G. C. Hall, S. Karaman, E. Frazzoli, and D. Rus, “Least- violating control strategy synthesis with safety rules,” in Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control, ser. HSCC ’13. New York, NY, USA: ACM, 2013, pp. 1–10. [Online]. Available: http://doi.acm.org/10.1145/2461328.2461330 J. Tumova, L. I. R. Castro, S. Karaman, E. Frazzoli, and D. Rus, “Minimum-violation LTL Planning with Conflicting Specifications,” American Control Conference, jan 2013. [Online]. Available: http://arxiv.org/abs/1303.3679
[21] C. I. Vasile, J. Tumova, S. Karaman, C. Belta, and D. Rus, “Minimum- violation scLTL motion planning for mobility-on-demand,” Proceedings - IEEE International Conference on Robotics and Automation, pp. 1481–1488, 2017.
E. Frazzoli and K. Iagnemma, “US patent US9645577B1: Facilitating vehicle driving and self-driving.”
J. W. Polderman and J. C. Willems, Introduction to Mathematical Systems Theory: A Behavioral Approach. Berlin, Heidelberg: Springer- Verlag, 1998.
R. C. Arkin, Behavior-based Robotics, 1st ed. Cambridge, MA, USA: MIT Press, 1998.
M. J. Mataric and F. Michaud, “Behavior-based systems,” in Springer Handbook of Robotics, 2008, pp. 891–909. [Online]. Available: https://doi.org/10.1007/978- 3- 540- 30301- 5_39
V. Modugno, G. Neumann, E. Rueckert, G. Oriolo, J. Peters, and S. Ivaldi, “Learning soft task priorities for control of redundant robots,” in IEEE International Conference on Robotics and Automation (ICRA 2016), Stockholm, Sweden, May 2016. [Online]. Available: https://hal.archives- ouvertes.fr/hal- 01273409
J. Silvério, S. Calinon, L. D. Rozo, and D. G. Caldwell, “Learning competing constraints and task priorities from demonstrations of bimanual skills,” CoRR, vol. abs/1707.06791, 2017. [Online]. Available: http://arxiv.org/abs/1707.06791
S. Shalev-Shwartz, S. Shammah, and A. Shashua, “On a formal model of safe and scalable self-driving cars,” 08 2017.
A. Aswani, H. Gonzalez, S. S. Sastry, and C. Tomlin, “Provably safe and robust learning-based model predictive control,” Automatica, vol. 49, no. 5, pp. 1216–1226, jan 2013. [Online]. Available: http: //linkinghub.elsevier.com/retrieve/pii/S0005109813000678papers3: //publication/doi/10.1016/j.automatica.2013.02.003

[10] Reference

[1] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein GAN. arXiv preprint arXiv:1701.07875, 2016. 1
[2] J. Bao, D. Chen, F. Wen, H. Li, and G. Hua. CVAE-GAN: Fine-grained image generation through assymetric training. In To appear in Proceedings of the International Conference on Computer Vision (ICCV), 2017. 1
[3] M. Bautista, A. Sanakoyeu, and B. Ommer. Deep unsuper- vised similarity learning using partially ordered sets. In The IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2017. 2
[4] M. Bautista, A. Sanakoyeu, E. Sutter, and B. Ommer. Cliquecnn: Deep unsupervised exemplar learning. In Pro- ceedings of the Conference on Advances in Neural Infor- mation Processing Systems (NIPS), Barcelona, 2016. MIT Press, MIT Press. 2
[5] B. Brattoli, U. Bu ̈chler, A. S. Wahl, M. E. Schwab, and B. Ommer. Lstm self-supervision for detailed behavior anal- ysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (BB and UB con- tributed equally), (BB and UB contributed equally), 2017. 2
[6] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multi- person 2d pose estimation using part affinity fields. In CVPR, 2017. 3, 5, 6
[7] Q. Chen and V. Koltun. Photographic image synthesis with cascaded refinement networks. In To appear in Proceedings of the International Conference on Computer Vision (ICCV), 2017. 1, 2, 3
[8] X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: Interpretable representation learn- ing by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657, 2016. 1
[9] M. Eitz, J. Hays, and M. Alexa. How do humans sketch ob- jects? ACM Trans. Graph. (Proc. SIGGRAPH), 31(4):44:1– 44:10, 2012. 6
[10] I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio. Generative adversarial nets. In In Neural Information Pro- cessing Systems (NIPS), pages 2672–2680, 2014. 1, 2
[11] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. In Computer Vision - ECCV 2016 - 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV, pages 630–645, 2016. 5
[12] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image- to-image translation with conditional adversarial networks. arxiv preprint arXiv:1611.07004, 2016. 1, 2, 4, 5, 6, 3
[13] T. Karras, T. Aila, S. Laine, and J. Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. 1
[14] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014. 5
[15] D. P. Kingma, S. Mohamed, D. Jimenez Rezende, and M. Welling. Semi-supervised learning with deep generative models. In Z. Ghahramani, M. Welling, C. Cortes, N. D.
Lawrence, and K. Q. Weinberger, editors, Advances in Neu- ral Information Processing Systems 27, pages 3581–3589. Curran Associates, Inc., 2014. 2
[16] D. P. Kingma and M. Welling. Auto-encoding variational bayes. CoRR, abs/1312.6114, 2013. 1, 2, 3
[17] A. B. L. Larsen, S. K. Sønderby, and O. Winther. Autoen- coding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015. 1, 3
[18] C. Lassner, G. Pons-Moll, and P. V. Gehler. A generative model for people in clothing. In Proceedings of the IEEE International Conference on Computer Vision, 2017. 1, 2
[19] C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-realistic single image super resolution using generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 1
[20] T. Lin, M. Maire, S. J. Belongie, L. D. Bourdev, R. B. Gir- shick, J. Hays, P. Perona, D. Ramanan, P. Dolla ́r, and C. L. Zitnick. Microsoft COCO: common objects in context. arXiv preprint arXiv:1405.0312, 2014. 1, 4, 3, 10
[21] Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR), 2016. 1, 4, 3, 10
[22] Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Con- ference on Computer Vision (ICCV), 2015. 1
[23] Z. Liu, S. Yan, P. Luo, X. Wang, and X. Tang. Fashion land- mark detection in the wild. In European Conference on Com- puter Vision (ECCV), 2016. 1, 4, 3, 10
[24] L. Ma, X. Jia, Q. Sun, B. Schiele, T. Tuytelaars, and L. Van Gool. Pose guided person image generation. In To appear in Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS), pages 3846– 3854,2017. 1,2,4,5,6,7,8
[25] T. Milbich, M. Bautista, E. Sutter, and B. Ommer. Unsuper- vised video understanding by reconciliation of posture sim- ilarities. In Proceedings of the IEEE International Confer- ence on Computer Vision (ICCV), 2017. 2
[26] A. Odena, C. Olah, and J. Shlens. Conditional image synthesis with auxiliary classifier gans. arXiv preprint arXiv:1610.09585, 2017. 2
[27] A. Radford, L. Metz, and S. Chintala. Unsupervised repre- sentation learning with deep convolutional generative adver- sarial networks. In In International Conference On Learning Representations (ICLR), 2016. 1
[28] S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele, and H. Lee. Learning what and where to draw. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, edi- tors, Advances in Neural Information Processing Systems 29, pages 217–225. Curran Associates, Inc., 2016. 2
[29] S. E. Reed, A. van den Oord, N. Kalchbrenner, S. Go ́mez, Z. Wang, D. Belov, and N. de Freitas. Parallel multiscale au- toregressive density estimation. In Proceedings of The 34th International Conference on Machine Learning, 2017. 2
[30] O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolu- tional Networks for Biomedical Image Segmentation, pages 234–241. Springer International Publishing, Cham, 2015. 1, 2,4
[31] M. Rosca, B. Lakshminarayanan, D. Warde-Farley, and S. Mohamed. Variational approaches for auto-encoding gen- erative adversarial networks. CoRR, abs/1706.04987, 2017. 3
[32] J. C. Rubio, A. Eigenstetter, and B. Ommer. Generative reg- ularization with latent topics for discriminative object recog- nition. Pattern Recognition, 48(12):3871–3880, 2015. 1
[33] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211–252, 2015. 7
[34] T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training gans. In NIPS, 2016. 6
[35] T. Salimans and D. P. Kingma. Weight normalization: A simple reparameterization to accelerate training of deep neu- ral networks. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Infor- mation Processing Systems 29, pages 901–909. Curran As- sociates, Inc., 2016. 5
[36] W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang. Real-time single im- age and video super-resolution using an efficient sub-pixel convolutional neural network. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1874–1883, 2016. 5
[37] K. Simonyan and A. Zisserman. Very deep convolu- tional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014. 3, 7
[38] K.Sohn,H.Lee,andX.Yan.Learningstructuredoutputrep- resentation using deep conditional generative models. In In Neural Information Processing Systems (NIPS), pages 3483– 3491, 2015. 1, 2
[39] A. van den Oord, N. Kalchbrenner, , L. E. K. Kavukcuoglu, O. Vinyals, and A. Graves. Conditional image generation with pixelcnn decoders. In In Neural Information Processing Systems (NIPS), pages 4790–4798, 2016. 2
[40] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. Trans. Img. Proc., 13(4):600–612, Apr. 2004. 6
[41] S. Xie and Z. Tu. Holistically-nested edge detection. In In Proceedings of the IEEE International Conference on Com- puter Vision (ICCV), 2015. 3, 5
[42] X. Yan, J. Yang, K. Sohn, and H. Lee. Attribute2image: Conditional image generation from visual attributes. In Pro- ceedings of the European Conference on Computer Vision, 2016. 2
[43] A. Yu and K. Grauman. Fine-grained visual comparisons wiht local learnings. In In Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 1, 4, 2
[44] H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. Metaxas. Stackgan: Text photo-realistic image synthesis
with stacked generative adversarial networks. In To appear in Proceedings of the International Conference on Computer Vision (ICCV), 2017. 2
[45] W. Zhang, M. Zhu, and K. G. Derpanis. From actemes to action: A strongly-supervised representation for detailed ac- tion understanding. In Proceedings of the IEEE International Conference on Computer Vision, pages 2248–2255, 2013. 7
[46] B. Zhao, X. Wu, Z. Cheng, H. Liu, and J. Feng. Multi- view image generation from a single-view. arXiv preprint arXiv:1704.04886, 2017. 2
[47] L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, and Q. Tian. Scalable person re-identification: A benchmark. In Com- puter Vision, IEEE International Conference on, 2015. 1, 4, 3, 10
[48] Z. Zheng, L. Zheng, and Y. Yang. A discriminatively learned cnn embedding for person re-identification. arXiv preprint arXiv:1611.05666, 2016. 7
[49] J.-Y.Zhu,P.Kra ̈henbu ̈hl,E.Shechtman,andA.A.Efros. Generative visual manipulation on the natural image mani- fold. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 1, 4, 2
[50] J.-Y. Zhu and T. Park. ImagetoImage Translation with con- ditional adversarial nets. 4
[51] J.-Y.Zhu,T.Park,P.Isola,andA.A.Efros.Unpairedimage- to-image translation using cycle-consistent adversarial net- works. arXiv preprint arXiv:1703.10593, 2017. 1
[52] S.Zhu,S.Fidler,R.Urtasun,D.Lin,andC.C.Loy.Beyour own prada: Fashion synthesis with structural coherence. In Proceedings of the IEEE International Conference on Com- puter Vision, 2017. 2

[11]

A.T. Kearney, Inc. (2016): How Automakers Can Survive the Self-Driving Era
The Brookings Institution (2017): Gauging investment in self-driving cars
EPO (2017): Patents and the Fourth Industrial Revolution
EUIPO and EPO (2016): IPR-intensive industries and economic performance in the European Union
European Automobile Manufacturers’ Association (ACEA) (2018): The Automobile Industry Pocket Guide
Organisation Internationale des Constructeurs d’Automobiles (OICA): 2017 Production Statistics

[14]

Sarter, N.B., Woods, D.D.: How in the world did we ever get into that mode? Mode error and awareness in supervisory control. Hum. Factors 37, 5–19 (1995)
Sarter, N.B.: Investigating mode errors on automated flight decks. Illustrating the problem-driven, cumulative, and interdisciplinary nature of human factors research. Hum. Factors 50, 506–510 (2008)
Dornheim, M.A.: Dramatic incidents highlight mode problems in cockpits. Aviat. Week Space Technol. 142(5), 57–59 (1995)
Sarter, N.B., Woods, D.D.: Pilot interaction with cockpit automation II. An experimental study of pilots’ model and awareness of the flight management system. Int. J. Aviat. Psychol. 4, 1–28 (1994)
Sarter, N.B., Woods, D.D.: Mode error in supervisory control of automated systems. Hum. Factors 36, 26–29 (1992)
Monk, A.: Mode errors. A user-centred analysis and some preventative measures using keying-contingent sound. Int. J. Man Mach. Stud. 24, 313–327 (1986)
Sellen, A., Kurtenbach, G., Buxton, W.: The prevention of mode errors through sensory feedback. Hum. Comput. Interact. 7, 141–164 (1992)
SAE International: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. SAE International, Warrendale (2014)
Spießl, W.: Assessment and Support of Error Recognition in Automated Driving. Universitätsbibliothek der Ludwig-Maximilians-Universität, München (2011)
ISO 26022: Road vehicles—Ergonomic aspects of transport information and control systems—Simulated lane change test to assess in-vehicle secondary task demand (2010)
Buld, S., Krüger, H.-P.: Wirkungen von Assistenz und Automation auf Fahrerzustand und Fahrsicherheit. Projekt: EMPHASIS, Effort-Management und Performance-Handling in sicherheitsrelevanten Situationen. Abschlussbericht, Würzburg (2002)
Merat, N., Jamson, A.H., Lai, F.C.H., Carsten, O.: Highly automated driving, secondary task performance, and driver state. Hum. Factors 54, 762–771 (2012)
Caird, J.K., Willness, C.R., Steel, P., Scialfa, C.: A meta-analysis of the effects of cell phones on driver performance. Accid. Anal. Prev. 40, 1282–1293 (2008)
Strayer, D.L., Johnston, W.A.: Driven to distraction: dual-task studies of simulated driving and conversing on a cellular telephone. Psychol. Sci. 12, 462–466 (2001)
Strayer, D.L., Cooper, J.M., Turrill, J., Coleman, J.R., Medeiros-Ward, N., Biondi, F.: Measuring cognitive distraction in the automobile. In: AAA Foundation for Traffic Safety, 53ff (2013)

[16]

Abraham, Hillary, Bryan Reimer, Bobbie Seppelt, Craig Fitzgerald, Bruce Mehler, and Joseph F.
Coughlin, Consumer Interest in Automation: Preliminary Observations Exploring a Year’s
Change, Cambridge, Mass.: Massachusetts Institute of Technology AgeLab, white paper,
May 25, 2017. As of September 5, 2018:
http://agelab.mit.edu/sites/default/files/
MIT%20-%20NEMPA%20White%20Paper%20FINAL.pdf
Almklov, Petter G., Ragnar Rosness, and Kristine Størkersen, “When Safety Science Meets the
Practitioners: Does Safety Science Contribute to Marginalization of Practical Knowledge?”
Safety Science, Vol. 67, 2014, pp. 25–36.
American Center for Mobility, homepage, undated. As of September 5, 2018:
http://www.acmwillowrun.org
Amersbach, Christian, and Hermann Winner, Functional Decomposition: An Approach to
Reduce the Approval Effort for Highly Automated Driving, Darmstadt, Germany: Institute of
Automotive Engineering, 2017.
Anderson, James M., Nidhi Kalra, Karlyn D. Stanley, Paul Sorensen, Constantine Samaras, and
Tobi A. Oluwatola, Autonomous Vehicle Technology: A Guide for Policymakers, Santa
Monica, Calif.: RAND Corporation, RR-443-2-RC, 2016. As of August 31, 2018:
https://www.rand.org/pubs/research_reports/RR443-2.html
Apollo homepage, undated. As of September 4, 2018:
http://apollo.auto
Arai, Yuji, Tetsuya Nishimoto, Yukihiro Ezaka, and Kenichi Yoshimoto, “Accidents and Near-
Misses Analysis by Using Video Drive-Recorders in a Fleet Test,” in Proceedings of the 17th
International Technical Conference on the Enhanced Safety of Vehicles (ESV) Conference,
Amsterdam, June 4–7, 2001. As of September 4, 2018:
https://pdfs.semanticscholar.org/609b/99dbb5787267d7c6f1147206e1bb49731bf3.pdf
Association for the Advancement of Automotive Medicine, “Overview,” webpage, undated. As of September 4, 2018:
https://www.aaam.org/abbreviated-injury-scale-ais
Association for Standardization of Automation and Measuring Systems, “Kick-Off Workshop ASAM OpenDRIVE,” webpage, undated. As of September 2, 2018:
https://www.asam.net/conferences-events/detail/kick-off-workshop-asam-opendrive Auto Alliance homepage, undated. As of September 4, 2018:
https://autoalliance.org
Automotive Information Sharing and Analysis Center, homepage, undated. As of September 5, 2018:
https://www.automotiveisac.com
Aven, Terie, “What Is Safety Science?” Safety Science, Vol. 67, 2014, pp. 15–20.
Aviation Safety Reporting System, homepage, undated. As of September 5, 2018:
https://asrs.arc.nasa.gov
Banerjee, Subho S., Saurabh Jha, James Cyriac, Zbigniew T. Kalbarczyk, and Ravishankar K. Iyer, Hands Off the Wheel in Autonomous Vehicles? A Systems Perspective on Over a Million Miles of Field Data, Urbana, Ill.: University of Illinois at Urbana-Champaign, 2018.
Barber, Angela, “Status of Work in Process on ISO/SAE 21434 Automotive Cybersecurity Standard,” presentation, ISO SAE International, April 10, 2018. As of September 5, 2018:
https://www.sans.org/summit-archives/file/summit-archive-1525889601.pdf
Barry, Keith, “Automakers Embrace Over-the-Air Updates, but Can We Trust Digital Car
Repair?” Consumer Reports, April 20, 2018. As of September 5, 2018:
https://www.consumerreports.org/automotive-technology/automakers-embrace-over-the-airupdates-can-we-trust-digital-car-repair
Blanco, Maria, Jon Atwood, Sheldon Russell, Tammy Trimble, Julie McClafferty, and Miguel Perez, Automated Vehicle Crash Rate Comparison Using Naturalistic Data, Blacksburg, Va.: Virginia Tech Transportation Institute, 2016.
Boehm, Barry, “Verifying and Validating Software Requirements and Design Specifications,”
IEEE Software, January 1984, pp. 75–88.
Bosch Engineering, “Validation of Highly Automated and Autonomous Automobile Systems,”
briefing slides, Frankfurt, Oberhursel: Automotive Safety and Security Week Testing ADAS
and Self-Driving Cars, 2017.
Bryans, Jeremy, “From Automotive Safety to Automotive Security: Progress, Possibilities, and Pitfalls,” briefing slides, IPC, undated.
BusinessDictionary, “safety,” webpage, undated. As of September 2, 2018:
http://www.businessdictionary.com/definition/safety.html
California Code of Regulations, Title 13, Motor Vehicles, Division 1, Department of Motor Vehicles, Chapter 1, Department of Motor Vehicles, Article 3.7, Testing of Autonomous
Vehicles, Section 227.46, Reporting Disengagement of Autonomous Mode, undated. As of September 4, 2018:
https://www.dmv.ca.gov/portal/wcm/connect/d48f347b-8815-458e-9df2-
5ded9f208e9e/adopted_txt.pdf?MOD=AJPERES
California Department of Motor Vehicles, Annual Report of Autonomous Vehicle Disengagements, undated.
———, “Autonomous Vehicle Disengagement Reports 2017,” webpage, 2017. As of September 4, 2018:
https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/disengagement_report_2017
Centers for Disease Control and Prevention and Health Resources and Services Administration,
Integrated Guidance for Developing Epidemiologic Profiles: HIV Prevention and Ryan
White HIV/AIDS Programs Planning. Atlanta, Georgia, 2014. As of September 4, 2018:
https://www.cdc.gov/hiv/pdf/guidelines_developing_epidemiologic_profiles.pdf
Chao, Elaine, U.S. Secretary of Transportation, remarks at the Automated Vehicles Symposium 2018, San Francisco, Calif., July 10, 2018.
Chen, Wenjun, Peter Cooper, and Mario Pinili, “Driver Accident Risk in Relation to the Penalty
Point System in British Columbia,” Journal of Safety Research, Vol. 26, No. 1, 1995, pp. 9–18.
City of Boston, “Robot Block Party,” webpage, October 15, 2017. As of September 11, 2018:
https://www.boston.gov/calendar/robot-block-party
Consumer Reports, “Cars with Advanced Safety Systems.” June 29, 2018. As of September 5, 2018:
https://www.consumerreports.org/car-safety/cars-with-advanced-safety-systems
Defense Advanced Research Projects Agency, “The DARPA Grand Challenge: Ten Years
Later,” webpage, March 13, 2014. As of September 2, 2018:
https://www.darpa.mil/news-events/2014-03-13
Ecola, Liisa, Steven W. Popper, Richard Silberglitt, and Laura Fraade-Blanar, The Road to Zero: A Vision for Achieving Zero Roadway Deaths by 2050, Santa Monica, Calif.: RAND
Corporation, RR-2333-NSC, 2018, As of July 24, 2018:
https://www.rand.org/pubs/research_reports/RR2333.html
Els, Peter, “The First Level-3 Automated Vehicle Is on the Road: Is ISO Functional Safety and
Analysis in Step?” Automotive IQ, April 25, 2018. As of April 26, 2018:
https://www.automotive-iq.com/autonomous-drive/articles/first-level-3-automated-vehicleroad-iso-functional-safety-and-analysis
escar (Embedded Security in Cars), homepage, undated. As of September 5, 2018:
https://www.escar.info
Every, Joshua L., Frank Barickman, John Martin, Sughosh Rao, Scott Schnelle, and Bowen Weng, A Novel Method to Evaluate the Safety of Highly Automated Vehicles, presented at the
25th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National
Highway Traffic Safety Administration, Detroit, Michigan, 2017.
Evtimov, Ivan, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song, “Robust Physical-World Attacks on Deep Learning
Models,” Ground AI, Vol. 1, 2017. As of September 6, 2018:
https://www.groundai.com/project/robust-physical-world-attacks-on-deep-learning-models/
Federal Aviation Administration, Safety Risk Management Policy, Washington, D.C.: U.S.
Department of Transportation, Order 8040.4B, May 2, 2017.
———, “FAA Hotline Reporting Form,” August 13, 2018. As of September 5, 2018: https://hotline.faa.gov
Federal Highway Administration, Surrogate Safety Assessment Model and Validation: Final Report, McLean, Va.: U.S. Department of Transportation, February 2008. As of September 8, 2018:
https://rosap.ntl.bts.gov/view/dot/35896
Federal Ministry of Transport and Digital Infrastructure, Ethics Commission Automated and Connected Driving Report, Berlin, Germany, June 2017. As of September 2, 2018:
https://www.bmvi.de/SharedDocs/EN/publications/report-ethics-commission.pdf
Felton, Ryan, “California’s Autonomous Car Reports Are the Best in the Country—But Nowhere Near Good Enough,” Jalopnik, February 1, 2018. As of September 4, 2018:
https://jalopnik.com/californias-autonomous-car-reports-are-the-best-in-the-1822606953
Fischhoff, Baruch, Noel T. Brewer, and Julie S. Downs, eds., Communicating Risks and Benefits: An Evidence-Based User’s Guide, Silver Spring, Md.: Department of Health and Human Services, Food and Drug Administration, August 2011.
Fischhoff, Baruch, Paul Slovic, Sarah Lichtenstein, Stephen Read, and Barbara Combs, “How Safe is Safe Enough? A Psychometric Study of Attitudes Towards Technological Risks and
Benefits,” Policy Sciences, Vol. 9, No. 2, 1978, pp. 127–152.
Fraade-Blanar, Laura, and Nidhi Kalra, Autonomous Vehicles and Federal Safety Standards: An Exemption to the Rule? Santa Monica, Calif.: RAND Corporation, PE-258-RC, 2017. As of August 31, 2018: https://www.rand.org/pubs/perspectives/PE258.html
Frola, F. R., and C. O. Miller, System Safety in Aircraft Management, Washington D.C.:
Logistics Management Institute, 1984.
Gordis, Leon, Epidemiology, 5th ed., New York: Elsevier, 2014.
Government of Alberta, Leading Indicators for Workplace Health and Safety: A User Guide, 2015. As of September 4, 2018:
http://work.alberta.ca/documents/ohs-best-practices-BP019.pdf
Grim, Patrick, Robert Rosenberger, Adam Rosenfeld, Brian Anderson, and Robb E. Eason, “How Simulations Fail,” Synthese, Vol. 190, No. 12, 2013, pp. 2367–2390.
Haddon, Heather, “Kroger Plans to Introduce Driverless Grocery Deliveries,” Wall Street
Journal, June 28, 2018. As of September 4, 2018:
https://www.wsj.com/articles/kroger-plans-to-introduce-driverless-grocery-deliveries-1530190801
Haddon, William, Jr., “Advances in the Epidemiology of Injuries as a Basis for Public Policy,” Public Health Reports, Vol. 95, No. 5, 1980, pp. 411–421.
———, Approaches to Prevention of Injuries, paper presented at the American Medical
Association Conference on Prevention of Disabling Injuries, Miami, Fla., 1983.
Hartas, Dimitra, Educational Research and Inquiry: Qualitative and Quantitative Approaches, London: Bloomsbury Publishing, 2015.
Hawkins, Andrew J., “Senior Citizens Will Lead the Self-Driving Revolution,” The Verge, January 10, 2018. As of September 4, 2018:
https://www.theverge.com/2018/1/10/16874410/
voyage-self-driving-cars-villages-florida-retirement-communities
Hayward, John C., “Near Miss Determination Through Use of a Scale of Danger,” in Proceedings of the 51st Annual Meeting of the Highway Research Board, Washington, D.C., 1972, pp. 24–35.
Higgins, Tim, “Pizza Delivery Gears Up for Driverless Era,” Wall Street Journal, June 26, 2018. As of September 4, 2018:
https://www.wsj.com/articles/pizza-delivery-may-be-entering-a-new-era-1530029087
Hollnagel, Erik, “Is Safety a Subject for Science?” Safety Science, Vol. 67, 2014, pp. 21–24.
Hollnagel, Erik, Robert L. Wears, and Jeffrey Braithwaite, From Safety-I to Safety-II: A White Paper, Denmark: Resilient Health Care Net, published simultaneously by the University of
Southern Denmark, University of Florida, and Macquarie University, 2015. As of September 2, 2018:
https://www.england.nhs.uk/signuptosafety/wp-content/uploads/sites/16/2015/10/safety-1-safety-2-whte-papr.pdf
Hopkins, Andrew, “Issues in Safety Science,” Safety Science, Vol. 67, 2014, pp. 6–14.
Ingrassia, Jill, Managing Director, Government Relations & Traffic Safety Advocacy, AAA, remarks at the Automated Vehicles Symposium 2018, San Francisco, Calif., July 11, 2018.
Insurance Institute for Highway Safety, Highway Loss Data Institute, “Lane Maintenance Systems Still a Turnoff for Many Drivers,” Status Report, Vol. 52, No. 4, June 22, 2017a. As of September 5, 2018:
https://www.iihs.org/iihs/sr/statusreport/article/52/4/3
———, “General Statistics: Yearly Snapshot—2016,” webpage, December 2017b. As of September 11, 2018:
https://www.iihs.org/iihs/topics/t/general-statistics/fatalityfacts/overview-of-fatality-facts
———, “Distracted Driving,” webpage, May 2018. As of September 2, 2018:
http://www.iihs.org/iihs/topics/t/distracted-driving/qanda
International Council on Systems Engineering, Systems Engineering Handbook, Version 3.1, August 2007.
———, Systems Engineering Handbook: A Guide for System Lifecycle Processes and Activities,
4th ed., Hoboken, N.J., Wiley Publishing & Sons, 2015.
International Organization for Standardization, homepage, undated-a. As of September 2, 2018:
https://www.iso.org/standards.html
———, Road Vehicles—Safety of the Intended Functionality, Geneva, Switzerland, ISO 21448,
undated-b. As of September 2, 2018:
https://www.iso.org/standard/70939.html
———, Road Vehicles—Functional Safety, Part 1, Vocabulary, Geneva, Switzerland, ISO 26262, 2011. As of September 2, 2018:
https://www.iso.org/obp/ui/#iso:std:iso:26262ed-1:v1:en
International Organization for Standardization, International Electrotechnical Commission, and Institute of Electrical and Electronics Engineers, Systems and Software Engineering—Vocabulary, ISO/IEC/IEEE 24765:2010(E), 2010.
ISO—See International Organization for Standardization.
Jha, Saurabh, Subho Sankar Banerjee, James Cyriac, Zbigniew Kalbarczyk, and Ravishankar K.
Iyer, AVFI, Fault Injection for Autonomous Vehicles, Luxembourg, IEEE/IFIP International
Conference on Dependable Systems and Networks, June 2018. As of September 2, 2018:
https://www.researchgate.net/publication/
325670464_AVFI_Fault_Injection_for_Autonomous_Vehicles
Johnsson, Carl, Aliaksei Laureshyn, and Tim De Ceunynck, “In Search of Surrogate Safety Indicators for Vulnerable Road Users: A Review of Surrogate Safety Indicators,
Transportation Reviews, Vol. 38, No. 5, 2018. As of September 7, 2018:
https://www.tandfonline.com/doi/pdf/10.1080/01441647.2018.1442888?needAccess=true& Kalra, Nidhi, and David G. Groves, The Enemy of Good: Estimating the Cost of Waiting for Nearly Perfect Automated Vehicles, Santa Monica, Calif.: RAND Corporation, RR-2150-RC,
2017. As of August 31, 2018:
https://www.rand.org/pubs/research_reports/RR2150.html
Kalra, Nidhi, and Susan M. Paddock, Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability? Santa Monica, Calif.: RAND
Corporation, RR-1478-RC, 2016. As of August 31, 2018:
https://www.rand.org/pubs/research_reports/RR1478.html
Kim, Anita, David Perlman, Dan Bogard, and Ryan Harrington, Review of Federal Motor Vehicle Safety Standards (FMVSS) for Automated Vehicles: Identifying Potential Barriers and Challenges for the Certification of Automated Vehicles Using Existing FMVSS, Washington, D.C.: John A. Volpe National Transportation Systems Center, U.S. Department
of Transportation, Preliminary Report, March 2016.
Klauer, Sheila G., Feng Guo, Bruce G. Simons-Morton, Marie Claude Ouimet, Susan E. Lee, and Thomas A. Dingus, “Distracted Driving and Risk of Road Crashes Among Novice and
Experienced Drivers,” New England Journal of Medicine, Vol. 370, No. 1, 2014, pp. 54–59.
Koopman, Philip, The Heavy Tail Safety Ceiling, Greenville, S.C.: Automated and Connected
Vehicle Systems Testing Symposium, June 20–21, 2018.
Koopman, Philip, and Michael Wagner, Challenges in Autonomous Vehicle Testing and
Validation, SAE World Congress, 2016a.
———, “Autonomous Vehicle Safety: An Interdisciplinary Challenge,” IEEE Intelligent Transportation Systems Magazine, Vol. 9, No. 1, June 2016b, pp. 90–96.
———, Toward a Framework for Highly Automated Vehicle Safety Validation, Detroit, Mich.: 2018 SAE World Congress, SAE 2018-01–1071, April 10–12, 2018. As of September 2, 2018:
https://users.ece.cmu.edu/~koopman/pubs/koopman18_av_safety_validation.pdf
Kriebel, David, Joel Tickner, Paul Epstein, John Lemons, Richard Levins, Edward L. Loechler,
Margaret Quinn, Ruthann Rudel, Ted Schettler, and Michael Stoto, “The Precautionary
Principle in Environmental Science,” Environmental Health Perspectives, Vol. 109, No. 9,
2001, pp. 871–876.
Lappin, Jane, email with authors, June 11–August 24, 2018.
Laris, Michael, “Federal Researchers Are Using Data from Waze and Maryland to Try to Predict
Road Dangers,” Washington Post, 2018.
Le Coze, Jean-Chrisophe, Kenneth Pettersen, and Teemu Reiman, “The Foundations of Safety Science,” Science Direct, Vol. 67, 2014, pp. 1–5.
Leveson, Nancy, “A New Accident Model for Engineering Safer Systems,” Safety Science, Vol. 42, No. 4, April 4, 2004, pp. 237–270.
———, Engineering a Safer World: Systems Thinking Applied to Safety, Cambridge, Mass.: Massachusetts Institute of Technology Press, 2011. As of September 7, 2018:
https://mitpress.mit.edu/books/engineering-safer-world
Lightstone, Amy S., Corinne Peek-Asa, and Jess F. Kraus, “Relationship Between Drivers
Record and Automobile Versus Child Pedestrian Collisions,” Injury Prevention, Vol. 3, No. 4, 1997, pp. 262–266.
Loewenstein, George, “Out of Control: Visceral Influences on Behavior,” Organizational Behavior and Human Decision Processes, Vol. 65, No. 3, March 1996, pp. 272–292.
Lombaerts, Thomas, Stefan Schuet, Diana Acosta, and John Kaneshige, “On-Line Flight Envelope Determination for Impaired Aircraft,” in Thaddäus Baier and Matthias Heller, eds.,
Robust Lateral Control of Future Small Aircraft, New York: Springer, 2015, pp. 263–282. As of September 11, 2018:
https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20150022358.pdf
Lui, K.-J., and P. Marchbanks, “A Study of the Time Between Previous Traffic Infractions and Fatal Automobile Crashes, 1984–1986,” Journal of Safety Research, Vol. 21, No. 2, 1990, pp. 45–51.
Maddox, John, Role of Proving Grounds in Three-Mode AV Validation Methodology, paper presented at the Automated Vehicles Symposium, San Francisco, July 11, 2018.
Madrigal, Alexis C., “Inside Waymo’s Secret World for Training Self-Driving Cars,” The Atlantic, August 23, 2017. As of September 5, 2018:
https://www.theatlantic.com/technology/archive/2017/08/
inside-waymos-secret-testing-and-simulation-facilities/537648/
Mahmud, S.M. Sohel, Luis Ferreira, Shamsul Hoque, and Ahmad Tavassoli, “Application of Proximal Surrogate Indicators for Safety Evaluation: A Review of Recent Developments and
Research Needs,” IATSS Research, Vol. 41, No. 4, December 2017, pp. 153–163. As of September 7, 2018:
https://www.sciencedirect.com/science/article/pii/S0386111217300286
Marshall, Aarian, “This Startup Would Like Self-Driving Car Companies to Share Data,” Wired,
August 7, 2018. As of September 5, 2018:
https://www.wired.com/story/scale-ai-label-share-self-driving-data/
Merriam-Webster, “Safety,” webpage, August 30, 2018. As of September 2, 2018:
https://www.merriam-webster.com/dictionary/safety
Miller, Benjamin M., Frank Camm, Marjory S. Blumenthal, Jesse Lastunen, and Kenneth W. Miller, Inching Toward Reform: Trump’s Deregulation and Its Implementation, Santa Monica, Calif.: RAND Corporation, PE-241-RC, 2017. As of September 2, 2018:
https://www.rand.org/pubs/perspectives/PE241.html
Möller, Niklas, Sven Ove Hansson, and Martin Peterson, “Safety Is More Than the Antonym of Risk,” Journal of Applied Philosophy, Vol. 23, No. 4, 2006, pp. 419–432.
Morgenstern, Hal, and Duncan Thomas, “Principles of Study Design in Environmental
Epidemiology,” Environmental Health Perspectives, Vol. 101, No. 4, 1993, pp. 23–38.
Moynihan, Ray, Lisa Bero, Dennis Ross-Degnan, David Henry, Kirby Lee, Judy Watkins,
Connie Mah, and Stephen B. Soumerai, “Coverage by the News Media of the Benefits and
Risks of Medications,” New England Journal of Medicine, Vol. 342, No. 22, 2000, pp. 1645–1650.
Mynatt, Clifford R., Michael E. Doherty, and Ryan D. Tweney, “Confirmation Bias in a Simulated Research Environment: An Experimental Study of Scientific Inference,” Quarterly
Journal of Experimental Psychology, Vol. 29, No. 1, 1977, pp. 85–95.
Nadimia, Navid, Hamid Behbahania, and Hamid Resa Shahbazib, 2016; “Calibration and Validation of a New Time-Based Surrogate Safety Measure Using Fuzzy Inference System,”
Journal of Traffic and Transportation Engineering, Vol. 3, No. 1, February 2016, pp. 51–58. As of September 7, 2018:
https://www.sciencedirect.com/science/article/pii/S2095756415200153
National Highway Traffic Safety Administration, “Automated Vehicles for Safety,” webpage, undated-a. As of August 31, 2018:
https://www.nhtsa.gov/technology-innovation/
automated-vehicles-safety#issue-road-self-driving
———, “Automatic Emergency Braking,” webpage, safercar.gov, undated-b. As of September 2, 2018:
https://www.safercar.gov/Vehicle-Shoppers/Safety-Technology/AEB/aeb
———, “Event Data Recorder,” webpage, undated-c. As of September 2, 2018:
https://www.nhtsa.gov/research-data/event-data-recorder
———, “OVSC Compliance Test Laboratories,” webpage, undated-d. As of September 2, 2018:
https://one.nhtsa.gov/Vehicle-Safety/OVSC-Compliance-Test-Laboratories
———, “Regulations,” webpage, undated-e. As of September 2, 2018:
https://www.nhtsa.gov/laws-regulations/fmvss
———, “Test Procedures,” webpage, undated-f. As of September 2, 2018:
https://one.nhtsa.gov/Vehicle-Safety/Test-Procedures
———, “Test Specification Forms,” webpage, undated-g. As of September 2, 2018:
https://one.nhtsa.gov/Vehicle-Safety/Test-Procedures/Test-Specification-Forms
———, The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the
100-Car Naturalistic Driving Study Data, Washington, D.C.: U.S. Department of
Transportation, DOT HS 810 594, 2006. As of September 4, 2018:
https://vtechworks.lib.vt.edu/bitstream/handle/10919/55090/DriverInattention.pdf
———, Pre-Crash Scenario for Crash Avoidance Research, Washington, D.C.: U.S.
Department of Transportation, DOT HS 810 767, 2007. As of September 2, 2018:
https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/pre-crash_scenario_typologyfinal_
pdf_version_5-2-07.pdf
———, Safety-Related Defects and Automated Safety Technologies, Washington, D.C.: U.S.
Department of Transportation, NHTSA Enforcement Guidance Bulletin 2016-02, Docket No.
NHTSA-2016-0040, 2016a.
———, “Accelerating the Next Revolution in Roadway Safety,” Washington, D.C., U.S.
Department of Transportation, Federal Automated Vehicles Policy, September 2016b.
———, Motor Vehicle Safety Defects and Recalls: What Every Vehicle Owner Should Know,
Washington, D.C.: U.S. Department of Transportation, DOT HS 808 795, August 2017a.
———, Automated Driving Systems 2.0: A Vision for Safety, Washington, D.C.: U.S.
Department of Transportation, DOT HS 812 442, September 2017b. As of September 2,2018:
https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/13069aads2.0_090617_v9a_tag.pdf
———, “Voluntary Safety Self-Assessment Template,” September 2017c. As of September 2,2018:
https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/voluntary_safety_selfassessment_for_web_101117_v1.pdf
———, “Monthly Compliance Status Reports,” webpage, 2018a. As of September 2, 2018:
https://icsw.nhtsa.gov/cars/testing/comply/monthly/
———, Removing Regulatory Barriers for Vehicles with Automated Driving Systems, Washington, D.C.: U.S. Department of Transportation, proposed rule, January 18, 2018b.
———, “Consumer Advisory: NHTSA Deems ‘Autopilot Buddy’ Product Unsafe,” press release, June 18, 2018c. As of September 5, 2018:
https://www.nhtsa.gov/press-releases/consumer-advisory-nhtsa-deems-autopilot-buddyproduct-unsafe
———, “Summary of Motor Vehicle Crashes,” fact sheet, August 2018d. As of September 11,
2018:
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812580
National Institutes of Health, Theory at a Glance: A Guide for Health Promotion Practice, 2nd ed., Washington, D.C., 2005.
National Research Council, Improving Risk Communication, Washington, D.C.: National Academies, 1989.
———, Understanding Risk: Informing Decisions in a Democratic Society, Washington, D.C.: The National Academies Press, 1996.
National Safety Council, “Injury Facts,” webpage, undated-a. As of September 2, 2018:
https://injuryfacts.nsc.org
———, “2017 Estimates Show Vehicle Fatalities Topped 40,000 for Second Straight Year,”
webpage, undated-b. As of September 11, 2018:
https://www.nsc.org/road-safety/safety-topics/fatality-estimates
National Transportation Safety Board, “Report an Aircraft Incident to the NTSB,” webpage,
undated. As of September 5, 2018:
https://www.ntsb.gov/Pages/Report.aspx
NHTSA—See National Highway Traffic Safety Administration.
Nowakowski, Christopher, Steven E. Shladover, and Ching-Yao Chan, “Determining the Readiness of Automated Driving Systems for Public Operation: Development of Behavioral
Competency Requirements,” Transportation Research Record: Journal of the Transportation
Research Board, Vol. 2559, 2016, pp. 65–72.
Nowakowski, Christopher, Steven E. Shladover, Ching-Yao Chan, and Han-Shue Tan,
“Development of California Regulations to Govern Testing and Operation of Automated
Driving Systems,” Transportation Research Record: Journal of the Transportation Research
Board, Vol. 1, 2015, pp. 1–16.
Nuro, Delivering Safety, Nuro’s Approach, Mountain View, Calif., 2018. As of September 20,
2018:
https://static1.squarespace.com/static/57bcb0e02994ca36c2ee746c/t/5b9a00848a922d8eaecf6
5a2/1536819358607/delivering_safety_nuros_approach.pdf
O’Neill, Brian, “Preventing Passenger Vehicle Occupant Injuries by Vehicle Design—A
Historical Perspective from IIHS,” Traffic Injury Prevention, Vol. 10, No. 2, 2009, pp. 113–126.
Occupational Safety and Health Administration, “The Business Case For Safety: Adding Value
and Competitive Advantage,” presentation, Georgetown University, March 2005. As of September 4, 2018:
https://www.osha.gov/dcsp/success_stories/compliance_assistance/
abbott/abbott_casestudies/index.html
———, “The Use of Metrics in Process Safety Management (PSM) Facilities,” fact sheet,
October 2016. As of September 4, 2018:
https://www.osha.gov/Publications/OSHA3896.pdf
Organisation for Economic Co-operation and Development, Safer Roads with Automated
Vehicles, Paris, France: International Transport Forum, 2018.
Partnership for Transportation Innovation and Opportunity, homepage, undated. As of September 4, 2018: https://ouravfuture.org/
Pasztor, Andy, “NASA Safety Watchdogs Raise Concerns About SpaceX, Boeing Spacecraft,” Wall Street Journal, January 12, 2018.
Peden, Margie, Richard Scurfield, David Sleet, Dinesh Mohan, Adnan A. Hyder, Eva Jarawan, and Colin D. Mathers, World Report on Road Traffic Injury Prevention, Geneva, Switzerland: World Health Organization, 2004.
PEGASUS Research Project, homepage, undated-a. As of September 2, 2018:
https://www.pegasusprojekt.de/en/home
———, “About,” undated-b. As of September 2, 2018:
https://www.pegasusprojekt.de/en/about-PEGASUS
Perrow, Charles, Normal Accidents: Living with High Risk Technologies, revised edition,
Princeton, N.J.: Princeton University Press, 1999.
Prescott, Al, Associate General Counsel for Tesla, “Autonomous Mode Disengagements for
Reporting Year 2017: Tesla,” memorandum to California Department of Motor Vehicles,
December 31, 2017. As of September 4, 2018:
https://www.dmv.ca.gov/portal/wcm/connect/f965670d-6c03-46a9-9109-
0c187adebbf2/Tesla.pdf?MOD=AJPERES&CVID=
Rahwan, Iyad, “Society-in-the-Loop: Programming the Algorithmic Social Contract,” Ethics and
Information Technology, Vol. 20, No. 1, 2018, pp. 5–14.
Renn, Ortwin, and Debra Levine, “Credibility and Trust in Risk Communication,” in Roger E.
Kasperson and Pieter Jan M. Stallen, eds., Communicating Risks to the Public, Boston:
Kluwer, 1991, pp. 175–217.
Roose, Kevin, “Can Ford Turn Itself into a Tech Company?” New York Times Magazine,
November 9, 2017. As of August 31, 2018:
https://www.nytimes.com/interactive/2017/11/09/magazine/tech-design-autonomous-futurecars-
detroit-ford.html
Runyan, Carol W., “Using the Haddon Matrix: Introducing the Third Dimension,” Injury
Prevention, Vol. 21, No. 2, 1998, pp. 302–307.
SAE International, homepage, undated. As of August 31, 2018:
https://www.sae.org/
SAE Mobilus, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, Warrendale, Pa., J3016_201609, September 30, 2016. As of August 31, 2018:
https://saemobilus.sae.org/content/j3016_201609
———, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, Warrendale, Pa., J3016_201806, June 15, 2018. As of August 31, 2018:
https://saemobilus.sae.org/content/J3016_201806/
Savitz, Scott, Henry H. Willis, Aaron C. Davenport, Martina Melliand, William Sasser, Elizabeth
Tencza, and Dulani Woods, Enhancing U.S. Coast Guard Metrics, Santa Monica, Calif.: RAND Corporation, RR-1173-USCG, 2015, As of May 25, 2018:
https://www.rand.org/pubs/research_reports/RR1173.html
Schoettle, Brandon, and Sivak, Michael, A Preliminary Analysis of Real-World Crashes Involving Self-Driving Vehicles, Ann Arbor, Mich.: University of Michigan Transportation Research Institute, October 2015.
Schwartz, Sharon, “The Fallacy of the Ecological Fallacy: The Potential Misuse of a Concept
and the Consequences,” American Journal of Public Health, Vol. 84, No. 5, 1994, pp. 819–824.
Self-Driving Coalition for Safer Streets, homepage, undated. As of September 4, 2018:
http://www.selfdrivingcoalition.org
Shalev-Shwartz, Shai, Shaked Shammah, and Amnon Shashua, On a Formal Model of Safe and
Scalable Self-Driving Cars, Jerusalem, Israel: Mobileye, 2017. As of September 4, 2018:
https://arxiv.org/pdf/1708.06374.pdf
Slovic, Paul, The Perception of Risk (Risk, Society, and Policy), London, England: Earthscan,
2000.
Smith, Aaron, and Monica Anderson, “Americans’ Attitudes Toward Driverless Vehicles,” in
Automation in Everyday Life, Washington, D.C.: Pew Research Foundation, October 4, 2017.
As of August 31, 2018:
http://www.pewinternet.org/2017/10/04/americans-attitudes-toward-driverless-vehicles/
Smith, Bryant Walker, “The Public Safety Case,” presentation, Law of the Newly Possible wiki,
July 18, 2016. As of September 5, 2018:
https://newlypossible.org/files/presentations/2016-07-18_PublicSafetyCase.pdf
“Standardization Efforts on Autonomous Driving Safety Barely Under Way,” Hansen Report on
Automotive Electronics, February 2017.
Statista, “Average Age of Passenger Cars in the U.S. from 1995 to 2016 (in years),” webpage,
2017. As of May 28, 2018:
https://www.statista.com/statistics/261877/average-age-of-passenger-cars-in-the-unitedstates/
Stern, Paul C., and Harvey V. Fineberg, eds., Understanding Risk: Informing Decisions in a
Democratic Society, Washington, D.C.: Committee on Risk Characterization, National
Research Council, 1996.
Stilgoe, Jack, “We Need New Rules for Self-Driving Cars,” Issues in Science and Technology,
Vol. 34, No. 3, 2018, pp. 52–57.
Stipancic, Joshua, Luis Miranda-Moreno, and Nicolas Saunier, “Vehicle Manoeuvers as
Surrogate Safety Measures: Extracting Data from the GPS-Enabled Smartphones of Regular
Drivers,” Accident Analysis and Prevention, Volume 115, June 2018, pp. 160–169. As of
September 11, 2018:
https://www.sciencedirect.com/science/article/pii/S000145751830109X
Teoh, Eric R., and David G. Kidd, “Rage Against the Machine? Google’s Self-Driving Cars
Versus Human Drivers,” Journal of Safety Research, Vol. 63, 2017, pp. 57–60.
Tisca, Ionela Adriana, Nicolae Istrat, Constantin Dan Dumitrescu, and Georgica Cornu, “Issues
Concerning the Road Safety Concept,” Procedia Economics and Finance, Vol. 39, 2016, pp.
441–445. As of September 2, 2018:
https://www.sciencedirect.com/science/article/pii/S221256711630346X
Uchida, Nobuyuki, Maki Kawakoshi, Takashi Tagawa, and Tsutomu Mochida, “An
Investigation of Factors Contributing to Major Crash Types in Japan Based on Naturalistic
Driving Data,” IATSS Research, Vol. 34, No. 1, 2010, pp. 22–30.
University of California PATH Program, Peer Review of Behavioral Competencies for AVs,
Berkeley, California: University of California, Berkeley, 2016.
U.S. Code, Title 49, Transportation, Subtitle VI, Motor Vehicle and Driver Programs, Part A,
General, Chapter 301, Motor Vehicle Safety.
U.S. Department of Defense, System Safety, Washington, D.C.: No. MIL-STD-8824, February
10, 2000.
U.S. Department of Transportation, “Draft AV Data Framework,” webpage, undated. As of
September 5, 2018:
https://www.transportation.gov/av/data/DraftAVDataFramework
———, “U.S. Department of Transportation Designates 10 Automated Vehicle Proving Grounds
to Encourage Testing of New Technologies,” webpage, January 19, 2017. As of September 2,
2018:
https://www.transportation.gov/briefing-room/dot1717
———, Roundtable on Data For Automated Vehicle Safety, Washington, D.C., summary report,
2018a.
———, “Data for Automated Vehicle Integration,” webpage, September 4, 2018b. As of August
2, 2018:
https://www.transportation.gov/av/data
Voyage, “Collaborating for a Safe Autonomous Future,” webpage, undated-a. As of September
5, 2018:
https://voyage.auto/open-autonomous-safety/
———, “Open-Sourcing Our Approach to Autonomous Safety,” webpage, undated-b. As of
September 4, 2018:
https://news.voyage.auto/open-sourcing-our-approach-to-autonomous-safety-434b1ab13a93
Wang, Chen, and Nikiforos Stamatiadis, “Evaluation of a Simulation-Based Surrogate Safety
Metric,” Accident Analysis and Prevention, Vol. 71, October 2014, pp. 82–92. As of September 11, 2018:
https://www.sciencedirect.com/science/article/pii/S0001457514001365
Waymo, Report on Autonomous Mode Disengagements for Waymo Self-Driving Vehicles in California, Mountain View, Calif.: Alphabet Inc., December 2016. As of September 4, 2018:
https://www.dmv.ca.gov/portal/wcm/connect/946b3502-c959-4e3b-b119-
91319c27788f/GoogleAutoWaymo_disengage_report_2016.pdf?MOD=AJPERES&CVID=%20%20%20Cf
———, On the Road to Fully Self-Driving, Mountain View, Calif.: Alphabet Inc., 2018. As of September 2, 2018:
https://storage.googleapis.com/sdc-prod/v1/safety-report/Safety%20Report%202018.pdf
Wing, Coady, Kosali Simon, and Ricardo A. Bello-Gomez, “Designing Difference in Difference Studies: Best Practices for Public Health Policy Research,” Annual Review of Public Health, Vol. 39, 2018, pp. 453–469.
Winkelman, Zev, Maya Buenaventura, James M. Anderson, Nahom Beyene, Pavan Katkar, and
Greg Baumann, When Hacked Autonomous Vehicles Do Damage, Who May Face Liability?
Santa Monica, Calif.: RAND Corporation, forthcoming.
Wolfram Language and System Documentation Center, “Heavy Tail Distributions,” webpage, undated. As of September 2, 2018:
http://reference.wolfram.com/language/guide/HeavyTailDistributions.html
World Bank, “Urban Transport Safety and Security,” in Cities on the Move, Washington, D.C., August 2002. As of September 2, 2018:
http://siteresources.worldbank.org/INTURBANTRANSPORT/Resources/chapter5.pdf
World Economic Forum and the Boston Consulting Group, Reshaping Urban Mobility with Autonomous Vehicles: Lessons from the City of Boston, Geneva: REF 140518, June 2018. As of September 11, 2018:
http://www3.weforum.org/docs/
WEF_Reshaping_Urban_Mobility_with_Autonomous_Vehicles_2018.pdf
Yoshida, Junko, “Uber Fatality Sends AVs Back to Safety 101,” EE Times, July 19, 2018. As of September 6, 2018:
https://www.eetimes.com/document.asp?doc_id=1333446

[17]

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. URL http://tensorflow.org/. Software available from tensorflow.org.
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mane. Concrete problems in ai safety. arXiv preprint arXiv:1606.06565, 2016.
C Angermueller and O Stegle. Multi-task deep neural network to predict CpG methylation profiles from low-coverage sequencing data. In NIPS MLCB workshop, 2015.
O Anjos, C Iglesias, F Peres, J Martínez, Á García, and J Taboada. Neural networks applied to discriminate botanical origin of honeys. Food chemistry, 175:128–136, 2015.
Christopher Atkeson and Juan Santamaria. A comparison of direct and model-based reinforcement learning. In In International Conference on Robotics and Automation. Citeseer, 1997.
Jimmy Ba and Brendan Frey. Adaptive dropout for training deep neural networks. In Advances in Neural Information Processing Systems, pages 3084–3092, 2013.
P Baldi, P Sadowski, and D Whiteson. Searching for exotic particles in high-energy physics with deep learning. Nature communications, 5, 2014.
Pierre Baldi and Peter J Sadowski. Understanding dropout. In Advances in Neural Information Processing Systems, pages 2814–2822, 2013.
David Barber and Christopher M Bishop. Ensemble learning in Bayesian neural networks. NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES, 168:215–238, 1998.
Justin Bayer, Christian Osendorfer, Daniela Korhammer, Nutan Chen, Sebastian Urban, and Patrick van der Smagt. On fast dropout and its applicability to recurrent networks. arXiv preprint arXiv:1311.0701, 2013.
Yoshua Bengio and Yann LeCun. Scaling learning algorithms towards ai. Large-scale
kernel machines, 34(5), 2007.
S Bergmann, S Stelzer, and S Strassburger. On the use of artificial neural networks in simulation-based manufacturing control. Journal of Simulation, 8(1):76–90, 2014.
James Bergstra et al. Theano: a CPU and GPU math expression compiler. In Pro- ceedings of the Python for Scientific Computing Conference (SciPy), June 2010. Oral Presentation.
Chris M Bishop. Training with noise is equivalent to Tikhonov regularization. Neural computation, 7(1):108–116, 1995.
Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. ISBN 0387310738.
Théodore Bluche, Christopher Kermorvant, and Jérôme Louradour. Where to apply dropout in recurrent neural networks for handwriting recognition? In ICDAR. IEEE, 2015.
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, 2015.
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurelie Neveol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation, pages 131–198, Berlin, Germany, August 2016. Association for Computational Linguistics.
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al. End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316, 2016.
Thang D Bui, Daniel Hernández-Lobato, Yingzhen Li, José Miguel Hernández-Lobato, and Richard E Turner. Deep Gaussian processes for regression using approximate expectation propagation. ICML, 2016.
Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder. Dropout distillation. In Proceedings of The 33rd International Conference on Machine Learning, pages 99–107, 2016.
Theophilos Cacoullos. On upper and lower bounds for the variance of a function of a random variable. The Annals of Probability, pages 799–809, 1982.
Hugh Chipman. Bayesian variable selection with related predictors.
Kyunghyun Cho et al. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In EMNLP, Doha, Qatar, October 2014. ACL.
François Chollet. Keras, 2015. URL https://github.com/fchollet/keras. GitHub repository.
David A Cohn, Zoubin Ghahramani, and Michael I Jordan. Active learning with statistical
models. Journal of artificial intelligence research, 1996.
George Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics
of control, signals and systems, 2(4):303–314, 1989.
Marc Deisenroth and Carl Rasmussen. PILCO: A model-based and data-efficient approach to policy search. In Proceedings of the 28th International Conference on machine learning (ICML-11), pages 465–472, 2011.
Marc Deisenroth, Dieter Fox, and Carl Rasmussen. Gaussian processes for data-efficient learning in robotics and control. Pattern Analysis and Machine Intelligence, IEEE
Transactions on, 37(2):408–423, 2015.
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248–255. IEEE, 2009.
John Denker and Yann LeCun. Transforming neural-net output levels to probability distributions. In Advances in Neural Information Processing Systems 3. Citeseer, 1991.
John Denker, Daniel Schwartz, Ben Wittner, Sara Solla, Richard Howard, Lawrence Jackel, and John Hopfield. Large automatic learning, rule extraction, and generalization.
Complex systems, 1(5):877–922, 1987.
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, and Steffen Udluft. Learning and policy search in stochastic dynamical systems with Bayesian neural networks. arXiv preprint arXiv:1605.07127, 2016.
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth. Hybrid Monte Carlo. Physics letters B, 195(2):216–222, 1987.
Linton G Freeman. Elementary applied statistics, 1965.
Michael C. Fu. Chapter 19 gradient estimation. In Shane G. Henderson and Barry L. Nelson, editors, Simulation, volume 13 of Handbooks in Operations Research and Management Science, pages 575 – 616. Elsevier, 2006.
Antonino Furnari, Giovanni Maria Farinella, and Sebastiano Battiato. Segmenting egocentric videos to highlight personal locations of interest. 2016.
Yarin Gal. A theoretically grounded application of dropout in recurrent neural networks. arXiv:1512.05287, 2015.
Yarin Gal and Zoubin Ghahramani. Bayesian convolutional neural networks with Bernoulli approximate variational inference. arXiv:1506.02158, 2015a.
Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Insights and applications. In Deep Learning Workshop, ICML, 2015b.
Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Representing
model uncertainty in deep learning. arXiv:1506.02142, 2015c.
Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Appendix.
arXiv:1506.02157, 2015d.
Yarin Gal and Zoubin Ghahramani. Bayesian convolutional neural networks with Bernoulli
approximate variational inference. ICLR workshop track, 2016a.
Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in
recurrent neural networks. NIPS, 2016b.
Yarin Gal and Zoubin Ghahramani. Dropout as a Bayesian approximation: Representing
model uncertainty in deep learning. ICML, 2016c.
Yarin Gal and Richard Turner. Improving the Gaussian process sparse spectrum approx- imation by representing uncertainty in frequency inputs. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015.
Yarin Gal, Mark van der Wilk, and Carl Rasmussen. Distributed variational inference in sparse Gaussian process regression and latent variable models. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 3257–3265. Curran Associates, Inc., 2014.
Yarin Gal, Rowan McAllister, and Carl E. Rasmussen. Improving PILCO with Bayesian neural network dynamics models. Data-Efficient Machine Learning workshop, ICML, April, 2016.
Carl Friedrich Gauss. Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. 1809.
Edward I George and Robert E McCulloch. Variable selection via gibbs sampling. Journal of the American Statistical Association, 88(423):881–889, 1993.
Edward I George and Robert E McCulloch. Approaches for Bayesian variable selection. Statistica sinica, pages 339–373, 1997.
J.D Gergonne. Application de la méthode des moindre quarrés a l’interpolation des suites. Annales des Math Pures et Appl, 6:242–252, 1815.
Z Ghahramani. Probabilistic machine learning and artificial intelligence. Nature, 521 (7553), 2015.
Z. Ghahramani and H. Attias. Online variational Bayesian learning. Slides from talk presented at NIPS 2000 Workshop on Online learning, 2000.
Ryan J Giordano, Tamara Broderick, and Michael I Jordan. Linear response methods for accurate covariance estimates from mean field variational Bayes. In Advances in Neural Information Processing Systems, pages 1441–1449, 2015.
Paul Glasserman. Monte Carlo methods in financial engineering, volume 53. Springer Science & Business Media, 2013.
Peter W Glynn. Likelihood ratio gradient estimation for stochastic systems. Communi-
cations of the ACM, 33(10):75–84, 1990.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. Book in preparation
for MIT Press, 2016. URL http://www.deeplearningbook.org.
Ian J Goodfellow, Aaron Courville, and Yoshua Bengio. Spike-and-slab sparse coding for
unsupervised feature discovery. arXiv preprint arXiv:1201.3382, 2012.
Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with
deep recurrent neural networks. In ICASSP. IEEE, 2013.
Alex Graves. Practical variational inference for neural networks. In NIPS, 2011.
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine. Continuous deep Q-learning with model-based acceleration. ICML, 2016.
Jose Miguel Hernandez-Lobato and Ryan Adams. Probabilistic backpropagation for scalable learning of Bayesian neural networks. In ICML, 2015.
José Miguel Hernández-Lobato, Yingzhen Li, Daniel Hernández-Lobato, Thang Bui, and Richard E Turner. Black-box alpha divergence minimization. In Proceedings of The 33rd International Conference on Machine Learning, pages 1511–1520, 2016.
S Herzog and D Ostwald. Experimental biology: Sometimes Bayesian statistics are better. Nature, 494, 2013.
Geoffrey E Hinton and Drew Van Camp. Keeping the neural networks simple by minimizing the description length of the weights. In COLT, pages 5–13. ACM, 1993.
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580, 2012.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 9(8), 1997.
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley. Stochastic variational inference. JMLR, 2013.
Alex Holub, Pietro Perona, and Michael C Burl. Entropy-based active learning for object recognition. In Computer Vision and Pattern Recognition Workshops, 2008.
CVPRW’08. IEEE Computer Society Conference on, pages 1–8. IEEE, 2008.
Kurt Hornik. Approximation capabilities of multilayer feedforward networks. Neural
networks, 4(2):251–257, 1991.
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. Bayesian active learning for classification and preference learning. arXiv preprint arXiv:1112.5745, 2011.
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Weinberger. Deep networks with stochastic depth. arXiv preprint arXiv:1603.09382, 2016.
Hemant Ishwaran and J Sunil Rao. Spike and slab variable selection: frequentist and
Bayesian strategies. Annals of Statistics, pages 730–773, 2005.
Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with Gumbel-
softmax. In Bayesian Deep Learning workshop, NIPS, 2016.
Chuanyi Ji, Robert R Snapp, and Demetri Psaltis. Generalizing smoothness constraints
from discrete samples. Neural Computation, 2(2):188–197, 1990.
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.
Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, and Lawrence K Saul. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233, 1999.
Ajay J Joshi, Fatih Porikli, and Nikolaos Papanikolopoulos. Multi-class active learning for image classification. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 2372–2379. IEEE, 2009.
Nal Kalchbrenner and Phil Blunsom. Recurrent continuous translation models. In EMNLP, 2013.
Michael Kampffmeyer, Arnt-Borre Salberg, and Robert Jenssen. Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2016.
A Karpathy et al. A Javascript implementation of neural networks. https://github.com/ karpathy/convnetjs, 2014–2015.
C.D. Keeling, T.P. Whorf, and the Carbon Dioxide Research Group. Atmospheric CO2 concentrations (ppmv) derived from in situ air samples collected at Mauna Loa Observatory, Hawaii. Scripps Institution of Oceanography (SIO), University of California, La Jolla, California USA 92093-0444, June 2004.
Alex Kendall and Roberto Cipolla. Modelling uncertainty in deep learning for camera relocalization. In 2016 IEEE International Conference on Robotics and Automation
(ICRA), pages 4762–4769. IEEE, 2016.
Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla. Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680, 2015.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
Diederik P Kingma and Max Welling. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013.
Diederik P Kingma and Max Welling. Stochastic gradient VB and the variational auto-encoder. 2nd International Conference on Learning Representationsm (ICLR), 2014.
Diederik P Kingma, Tim Salimans, and Max Welling. Variational dropout and the local reparameterization trick. In NIPS. Curran Associates, Inc., 2015.
Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Tech. Rep, 1(4):7, 2009.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron Courville, et al. Zoneout: Regularizing RNNs by randomly preserving hidden activations. arXiv preprint arXiv:1606.01305, 2016.
M Krzywinski and N Altman. Points of significance: Importance of being uncertain. Nature methods, 10(9), 2013.
Solomon Kullback. Information theory and statistics. John Wiley & Sons, 1959. Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of
mathematical statistics, 22(1):79–86, 1951.
Quoc V Le, Alex J Smola, and Stéphane Canu. Heteroscedastic Gaussian process regression. In Proceedings of the 22nd international conference on Machine learning, pages 489–496. ACM, 2005.
J. Lean. Solar irradiance reconstruction. NOAA/NGDC Paleoclimatology Program, Boulder CO, USA, 2004. IGBP PAGES/World Data Center for Paleoclimatology. Data Contribution Series 2004-035.
Yann LeCun and Corinna Cortes. The mnist database of handwritten digits, 1998.
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541–551, 1989.
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu. Deeply- supervised nets. arXiv preprint arXiv:1409.5185, 2014.
Adrien Marie Legendre. Nouvelles Methodes pour la Determination des Orbites des Come’tes. Paris, 1805.
Nicholas Léonard, Sagar Waghmare, and Yang Wang. RNN: Recurrent library for Torch. arXiv preprint arXiv:1511.07889, 2015.
Xin Li and Yuhong Guo. Adaptive active learning for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 859–866, 2013.
Yingzhen Li and Richard E Turner. Variational inference with r\’enyi divergence. arXiv preprint arXiv:1602.02311, 2016.
Timothy Lillicrap, Jonathan Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971, 2015.
Min Lin, Qiang Chen, and Shuicheng Yan. Network in network. arXiv preprint arXiv:1312.4400, 2013.
O Linda, T Vollmer, and M Manic. Neural network based intrusion detection system for critical infrastructures. In Neural Networks, 2009. IJCNN 2009. International Joint
Conference on. IEEE, 2009.
Christos Louizos. Smart regularization of deep architectures, 2015.
David MacKay. Bayesian methods for adaptive models. PhD thesis, California Institute of Technology, 1992a.
David JC MacKay. A practical Bayesian framework for backpropagation networks. Neural Computation, 4(3):448–472, 1992b.
Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The Concrete distribution: A continuous relaxation of discrete random variables. In Bayesian Deep Learning workshop, NIPS, 2016.
David Madigan and Adrian E Raftery. Model selection and accounting for model uncertainty in graphical models using Occam’s window. Journal of the American Statistical Association, 89(428):1535–1546, 1994.
Shin-ichi Maeda. A Bayesian encourages dropout. arXiv preprint arXiv:1412.7003, 2014. Andrew McHutchon. Nonlinear modelling and control using Gaussian processes. PhD
thesis, University of Cambridge, 2014.
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černocky`, and Sanjeev Khudanpur. Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association, 2010.
Tom Minka. Divergence measures and message passing. Technical report, Microsoft Research, 2005.
Toby J Mitchell and John J Beauchamp. Bayesian variable selection in linear regression. Journal of the American Statistical Association, 83(404):1023–1032, 1988.
V Mnih, K Kavukcuoglu, D Silver, A A Rusu, J Veness, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
Taesup Moon, Heeyoul Choi, Hoshik Lee, and Inchul Song. RnnDrop: A Novel Dropout for RNNs in ASR. In ASRU Workshop, 2015.
Radford M Neal. Bayesian learning for neural networks. PhD thesis, University of Toronto, 1995.
Radford M Neal. MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, 2:113–162, 2011.
Isaac Newton. Philosophiae naturalis principia mathematica, volume Adv.b.39.1. Jussu Societatis Regiæ ac Typis Joseph Streater, Londini, 1687. (in Latin).
NHTSA. PE 16-007. Technical report, U.S. Department of Transportation, National Highway Traffic Safety Administration, Jan 2017. Tesla Crash Preliminary Evaluation Report.
Steven J Nowlan and Geoffrey E Hinton. Simplifying neural networks by soft weight- sharing. Neural computation, 4(4):473–493, 1992.
Regina Nuzzo. Statistical errors. Nature, 506(13):150–152, 2014.
Manfred Opper and Cédric Archambeau. The variational Gaussian approximation
revisited. Neural Computation, 21(3):786–792, 2009.
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy. Deep explo- ration via bootstrapped DQN. In Advances In Neural Information Processing Systems, pages 4026–4034, 2016.
Marius Pachitariu and Maneesh Sahani. Regularization and nonlinearities for neural language models: when are they needed? arXiv preprint arXiv:1301.5650, 2013.
John Paisley, David Blei, and Michael Jordan. Variational Bayesian inference with stochastic search. ICML, 2012.
Vu Pham, Theodore Bluche, Christopher Kermorvant, and Jerome Louradour. Dropout improves recurrent neural networks for handwriting recognition. In ICFHR. IEEE, 2014.
Ofir Press and Lior Wolf. Using the output embedding to improve language models. arXiv preprint arXiv:1608.05859, 2016.
Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press, 2006. ISBN 026218253X.
Alfréd Rényi et al. On measures of entropy and information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. The Regents of the University of California, 1961.
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In ICML, 2014.
Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pages 400–407, 1951.
Donald B Rubin. The Bayesian bootstrap. The annals of statistics, 9(1):130–134, 1981.
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning internal representations by error propagation. Technical report, DTIC Document, 1985.
Masa-Aki Sato. Online model selection based on the variational Bayes. Neural Computa- tion, 13(7):1649–1681, 2001.
John Schulman, Nicolas Heess, Theophane Weber, and Pieter Abbeel. Gradient estimation using stochastic computation graphs. In Advances in Neural Information Processing Systems, pages 3528–3536, 2015.
Hilary L Seal. Studies in the history of probability and statistics. XXIX – The discovery of the method of least squares. Biometrika, 54(1-2):1–24, 1967.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Edinburgh neural machine transla- tion systems for wmt 16. In Proceedings of the First Conference on Machine Translation, pages 371–376, Berlin, Germany, August 2016. Association for Computational Linguis- tics.
Burr Settles. Active learning literature survey. University of Wisconsin, Madison, 52 (55-66):11, 2010.
Claude Elwood Shannon. A mathematical theory of communication. Bell System Technical Journal, 27(3):379–423, 1948.
Saurabh Singh, Derek Hoiem, and David Forsyth. Swapout: Learning an ensemble of deep architectures. NIPS, 2016.
Kirstine Smith. On the standard deviations of adjusted and interpolated values of an observed polynomial function and its constants and the guidance they give towards a proper choice of the distribution of the observations. Biometrika, 12:1–85, 1918.
Edward Snelson and Zoubin Ghahramani. Sparse Gaussian processes using pseudo-inputs. In Advances in neural information processing systems, pages 1257–1264, 2005.
Jasper Snoek, Hugo Larochelle, and Ryan P Adams. Practical Bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, pages 2951–2959, 2012. Jasper Snoek et al. Spearmint. https://github.com/JasperSnoek/spearmint, 2015.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhut- dinov. Dropout: A simple way to prevent neural networks from overfitting. JMLR, 2014.
Bradly C Stadie, Sergey Levine, and Pieter Abbeel. Incentivizing exploration in rein- forcement learning with deep predictive models. arXiv preprint arXiv:1507.00814, 2015.
Martin Sundermeyer, Ralf Schlüter, and Hermann Ney. LSTM neural networks for language modeling. In INTERSPEECH, 2012.
Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In NIPS, 2014.
Richard Sutton and Andrew Barto. Reinforcement learning: An introduction. MIT press, 1998.
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014.
William R Thompson. On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, pages 285–294, 1933.
Naftali Tishby, Esther Levin, and Sara A Solla. Consistent inference of probabilities in layered networks: Predictions and generalizations. In Neural Networks, 1989. IJCNN., International Joint Conference on, pages 403–409. IEEE, 1989.
Michalis Titsias and Miguel Lázaro-Gredilla. Doubly stochastic variational Bayes for non- conjugate inference. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1971–1979, 2014.
Michalis K Titsias and Miguel Lázaro-Gredilla. Spike and slab variational inference for multi-task and multiple kernel learning. In Advances in neural information processing systems, pages 2339–2347, 2011.
D Trafimow and M Marks. Editorial. Basic and Applied Social Psychology, 37(1), 2015.
R. E. Turner and M. Sahani. Two problems with variational expectation maximisation for time-series models. In Bayesian Time series models, chapter 5, pages 109–130. Cambridge University Press, 2011.
Stefan Wager, Sida Wang, and Percy S Liang. Dropout training as adaptive regularization. In Advances in Neural Information Processing Systems, pages 351–359, 2013.
L Wan, M Zeiler, S Zhang, Y LeCun, and R Fergus. Regularization of neural networks using dropconnect. In ICML-13, 2013.
S Wang and C Manning. Fast dropout training. ICML, 2013.
Andreas S Weigend, David E Rumelhart, and Bernardo A Huberman. Generalization by weight-elimination with application to forecasting. In Advances in Neural Information Processing Systems, pages 875–882, 1991.
Max Welling and Yee W Teh. Bayesian learning via stochastic gradient langevin dynamics. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 681–688, 2011.
Paul J Werbos. of backpropagation with application to a recurrent gas
market model. Neural Networks, 1(4):339–356, 1988.
Christopher KI Williams. Computing with infinite networks. Advances in neural infor-
mation processing systems, pages 295–301, 1997.
Ronald J Williams. Simple statistical gradient-following algorithms for connectionist
reinforcement learning. Machine learning, 8(3-4):229–256, 1992.
John Winn and Christopher M Bishop. Variational message passing. Journal of Machine
Learning Research, 6(Apr):661–694, 2005.
Xiao Yang, Roland Kwitt, and Marc Niethammer. Fast predictive image registration.
arXiv preprint arXiv:1607.02504, 2016.
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. Recurrent neural network regular-
ization. arXiv preprint arXiv:1409.2329, 2014.
Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In ICML 2003 workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 2003.

####[19]
Aeberhard, M., Schlichtharle, S., Kaempchen, N., & Bertram, T. (2012). Track-to-track fusion with asynchronous sensors using information matrix fusion for surround environment perception. IEEE Transactions on Intelligent Transportation Systems, 13(4), 1717–1726.
Ahmed, K. I. (1999). Modeling drivers’ acceleration and lane changing behavior (doctoral dissertation). Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, Massachusetts, USA.
Akamatsu, M., Green, P., & Bengler, K. (2013). Automotive technology and human factors research: Past, present, and future. International Journal of Vehicular Technology, 2013(3), 1–27.
Altendorf, Eugen, Baltzer, M., Kienle, M., Meier, S., Weißgerber, T., . . . Flemisch, F. (2015). H-mode 2d. In H. Winner, S. Hakuli, F. Lotz, & C. Singer (Eds.), Handbuch fahrerassistenzsysteme (pp. 1123–1138). Vieweg + Teubner.
Amditis, A., Lytrivis, P., Kotsiourou, C., Karaseitanidis, G., Grubb, G., & Thomaidis, G. (2013). Multi-sensor tracking and lane estimation in highly automated vehicles. IET Intelligent Transport Systems, 7(1), 160–169.
Baig, Q., Aycard, O., Vu, T. D., & Fraichard, T. (2011). Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario. IEEE Intelligent Vehicles Symposium (IV), 362–367.
Bajaj, R., Ranaweera, S., & Agrawal, D. (2002). Gps: location-tracking technology. Computer, 35(4), 92–94.
Bajracharya, M., Moghaddam, B., Howard, A., Brennan, S., & Matthies, L. H. (2009). A fast stereo-based system for detecting and tracking pedestrians from a moving vehicle. The International Journal of Robotics Research, 28(11-12), 1466–1485.
Bao, S., & Boyle, L. N. (2009). Age-related differences in visual scanning at median- divided highway intersections in rural areas. Accident Analysis & Prevention, 41(1), 146–152.
Basavanna, M. (2000). Dictionary of psychology. New Delhi: Allied Publishers.
Becker, J. C. (2000). Fusion of heterogeneous sensors for the guidance of an autonomous vehicle - information fusion, 2000. fusion 2000. proceedings of the third international conference on. Proceedings of the Third International Conference on Information Fusion, WED5/11 - WED5/18 vol.2.
Beller, J., Heesen, M., & Vollrath, M. (2013). Improving the driver-automation interaction: An approach using automation uncertainty. Human Factors: The Journal of the Human Factors and Ergonomics Society, 55(6), 1130–1141.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying
influential data and sources of collinearity. New York: Wiley.
Bengler, K. (2014). Driver distraction. Encyclopedia of Automotive Engineering, 1–8.
Bengler, K., Dietmayer, K., Farber, B., Maurer, M., Stiller, C., & Winner, H. (2014). Three decades of driver assistance systems: Review and future perspectives. IEEE Intelligent Transportation Systems Magazine, 6(4), 6–22.
Bengler, K., & Flemisch, F. (2011). Von h-mode zur kooperativen fahrzeugführung: Grundlegende ergonomische fragestellungen. 5. Darmstädter Kolloquium - Zukunft der Fahrzeugführung. Kooperativ oder autonom. Darmstadt, Germany.
Bereszewski, M. (2013). Automatisiertes fahren kommt - mit sicherheit. ATZextra, 18(13), 4–7.
Berisha, I. (2013). Reaktionszeiten beim hochautomatisierten fahren unter beanspruchung durch unterschiedliche nebenaufgaben (bachelor’s thesis). Tech- nical University of Munich, Institute of Ergonomics, Munich, Germany.
Bernotat, R. (1970). Operation functions in vehicle control, anthropoteehnik in der fahrzeugfuhrung. Ergonomics, 13(3), 353–377.
Bertozzi, M., & Broggi, A. (1998). Gold: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing, 7(1), 62–81.
Beukel, A. v. d., & Voort, M. C. v. d. (2011). Human-centered challenges and contri- bution for the implementation of automated driving. Advanced microsystems for automotive applications, 225–235.
Beukel, A. v. d., & Voort, M. C. v. d. (2013). The influence of time-criticality on situation awareness when retrieving human control after automated driving. Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems (ITSC 2013), 16, 2000–2005.
Beukel, A. v. d., & Voort, M. C. v. d. (2014a). Design considerations on user-interaction for semi-automated driving. FISITA 2014 world automotive congress. Maastricht, Netherlands, 35, 1–8.
Beukel, A. v. d., & Voort, M. C. v. d. (2014b). Driver’s situation awareness during supervision of automated control - comparison between sart and sagat measure- ment techniques. European Conference on Human Centred Design for Intelligent Transport Systems. Vienna, Austria, 4.
Blommer, M., Curry, R., Kochhar, D., Swaminathan, R., Talamonti, W., & Tijerina, L. (2015). The effects of a scheduled driver engagement strategy in automated driv- ing. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 1681–1685.
Bouibed, K., Aitouche, A., & Bayart, M. (2010). Sensor and actuator fault detection and isolation using two model based approaches: Application to an autonomous electric vehicle. 18th Mediterranean Conference on Control & Automation. Marrakech, Morocco, 18, 1290–1295.
Broggi, A., Buzzoni, M., Felisa, M., & Zani, P. (2011). Stereo obstacle detection in chal- lenging environments: The viac experience. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1599–1604.
Bubb, H. (2015a). Das regelkreisparadigma der ergonomie. In H. Bubb, K. Bengler, R. E. Grünen, & M. Vollrath (Eds.), Automobilergonomie (pp. 28–65). Springer Vieweg.
Bubb, H. (2015b). Einführung. In H. Bubb, K. Bengler, R. E. Grünen, & M. Vollrath (Eds.), Automobilergonomie (pp. 1–26). Springer Vieweg.
Buck, J. R. (2006). Learning and forgetting. In W. Karwowski (Ed.), International encyclopedia of ergonomics and human factors (pp. 767–778). CRC/Taylor & Francis.
Buehler, M., Iagnemma, K., & Singh, S. (2007). The 2005 darpa grand challenge: The great robot race (Vol. v. 36). Berlin: Springer.
Buehler, M., Iagnemma, K., & Singh, S. (2009). The darpa urban challenge: Autonomous vehicles in city traffic (Vol. v. 56). Berlin: Springer.
Burckhardt, M. (1985). Reaktionszeiten bei notbremsvorgängen. Köln: TÜV Rheinland.
Campbell, J. L., Richard, C. M., Brown, J. L., & McCallum, M. (2007). Crash warning system interfaces: Human factors insights and lessons learned (No. HS-810 697). Washington and D.C. and Springfield and Va: U.S. Department of Transportation and National Highway Traffic Safety Administration.
Cantin, V., Lavallière, M., Simoneau, M., & Teasdale, N. (2009). Mental workload when driving in a simulator: Effects of age and driving complexity. Accident Analysis & Prevention, 41(4), 763–771.
Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of human-computer interaction. Hillsdale and N.J: L. Erlbaum Associates.
Card, S. K., Moran, T. P., & Newell, A. (1986). The model human processor: An engineering model of human performance. Handbook of Perception and Human Performance, 2(UIR-R-1986-05), 1–45.
Carsten, O., Lai, F. C. H., Barnard, Y., Jamson, A. H., & Merat, N. (2012). Control task substitution in semiautomated driving: Does it matter what aspects are au- tomated? Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 747–761.
Chang, T.-H., & Yi-Ru Chen. (2014). Driver fatigue surveillance via eye detection. Intelligent Transportation Systems (ITSC), Qingdao, 366–371.
Chen, F. (2006). Designing human interface in speech technology. New York: Springer.
Chen, X., Kohlmeyer, B., Stroila, M., Alwar, N., Wang, R., & Bach, J. (2009). Next generation map making. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 488–491.
Chin-Teng, L., Ruei-Cheng, W., Sheng-Fu, L., Wen-Hung, C., Yu-Jie, C., & Tzyy-Ping, J. (2005). Eeg-based drowsiness estimation for safety driving using independent com- ponent analysis. IEEE Transactions on Circuits and Systems I: Regular Papers, 52(12), 2726–2738.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed ed.). Hillsdale and N.J: L. Erlbaum Associates.
Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science, 1(3), 98–101.
Cohen, J. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed ed.). Mahwah and N.J: L. Erlbaum Associates.
Colombaroni, C., & Fusco, G. (2014). Artificial neural network models for car following: Experimental analysis and calibration issues. Journal of Intelligent Transportation Systems, 18(1), 5–16.
Continental strategy focuses on automated driving. (2012). Re- trieved from: http://www.continental-corporation.com/www/pressportal com_en/themes/press_releases/1_topics/automated_driving_en/pr_2012 12_18_automated _driving_en.html: Continental AG.
Cook, R. D. (1977). Detection of influential observation in linear regression. Technomet- rics, 19(1), 15–18.
Crossman, E. R., & Szostak, H. T. (1968). Man-machine models for car-steering. California: University of California.
Dai Bin, Liu Xin, & Wu Tao. (2005). Fast stereo matching for autonomous vehicle navigation. IEEE International Conference on Vehicular Electronics and Safety, 117–121.
Damböck, D. (2013). Automationseffekte im fahrzeug – von der reaktion zur übernahme (doctoral dissertation). München: Technical University of Munich, Institute of Ergonomics, Munich, Germany.
Damböck, D., Farid, M., Tönert, L., & Bengler, K. (2012). Übernahmezeiten beim hochautomatisierten fahren. 5. Tagung Fahrerassistenz. Munich, Germany, 5.
Damböck, D., Kienle, M., Bengler, K., & Bubb, H. (2011). The h-metaphor as an example for cooperative vehicle driving. Proceedings of the 14th international con- ference on Human-computer interaction: towards mobile and intelligent interaction environments - Volumen Part III.
Darter, M., & Gordon, V. (2005). Vehicle steering control using modular neural networks. IEEE International Conference on Information Reuse and Intregration, 374–379.
Daza, I. G., Hernandez, N., Bergasa, L. M., Parra, I., Yebes, J. J., Gavilan, M., . . . Sotelo, M. A. (2011). Drowsiness monitoring based on driver and driving data fusion. 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 1199–1204.
Delaigue, P., & Eskandarian, A. (2004). A comprehensive vehicle braking model for predictions of stopping distances. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 218(12), 1409–1417.
Dickmann, J., Appenrodt, N., Bloecher, H.-L., Brenk, C., Hackbarth, T., Hahn, M., ... Sailer, A. (2014). Radar contribution to highly automated driving. 44th European Microwave Conference (EuMC), 1715–1718.
Dickmann, J., Appenrodt, N., & Brenk, C. (2014). Ieee spectrum: How we gave sight to the mercedes robotic car: Radar is the key to mercedes benz’s autonomous car. Retrieved from: http://spectrum.ieee.org/transportation/self-driving/how-we-gave- sight-to-the-mercedes-robotic-car: IEEE Spectrum.
Dieckmanns, E. (1989). Vehicle guidance by computer vision. In K. Linkwitz & U. Hangleiter (Eds.), High precision navigation (pp. 86–96). Springer Berlin Heidel- berg.
Donges, E. (1982). Aspekte der aktiven sicherheit bei der führung von personenkraftwa- gen. Automobilindustrie, 27(2), 182–190.
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., . . . Lautenbach, S. (2013). Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27–46.
Dziubek, N., Winner, H., Becker, M., & Leinen, S. (2012). Sensordatenfusion zur hochge- nauen ortung von kraftfahrzeugen mit integrierter genauigkeits- und integritätsbew- ertung der sensorsignale. 5. Tagung Fahrerassistenz. Munich, Germany.
Electronic Chauffeurs. (1959). Electronic chauffeurs are possibility on tomorrow’s highways. Electrical Engineering, 78(8), 875–876.
Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. Pro- ceedings of the Human Factors and Ergonomics Society Annual Meeting, 32(2), 97–101.
Endsley, M. R., & Kiris, E. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(2), 381–394.
Famoye, F., Wulu, J. T., & Singh, K. P. (2004). On the generalized poisson regression model with an application to accident data. Journal of Data Science, 2, 287–295.
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.
Feldhütter, A. (2015). Validierung eines regressionsmodells zur schätzung der param- eter einer übernahmesituation beim hochautomatisierten fahren (master’s thesis). Technical University of Munich, Institute of Ergonomics, Munich, Germany.
Felisa, M., & Zani, P. (2010). Robust monocular lane detection in urban environments. IEEE Intelligent Vehicles Symposium (IV). San Diego, United States, 591–596.
Flemisch, F. (2003). The h-metaphor as a guideline for vehicle automation and interac- tion (Nos. NASA/TM—2003-212672). Langley Research Center and Hampton and Virginia: National Aeronautics and Space Administration.
Flemisch, F., Bengler, K., Bubb, H., Winner, H., & Bruder, R. (2014). Towards cooperative guidance and control of highly automated vehicles: H-mode and conduct-by-wire. Ergonomics, 57(3), 343–360.
Flemisch, F., Nashashibi, F., Rauch, N., Schieben, A., Glaser, S., Temme, G., . . . Kauss- ner, A. (2010). Towards highly automated driving: Intermediate report on the haveit-joint system. European Road Transport Research Arena, 3.
Flemisch, F., Schieben, A., Schoemig, N., Strauss, M., Lueke, S., & Heyden, A. (2011). Design of human computer interfaces for highly automated vehicles in the eu-project haveit. Universal Access in Human-Computer Interaction. Context Diversity., 270– 279.
Franz, B., Kauer, M., Sebastian, G., & Hakuli, S. (2015). Conduct-by-wire. In H. Winner, S. Hakuli, F. Lotz, & C. Singer (Eds.), Handbuch fahrerassistenzsysteme (pp. 1111– 1121). Vieweg + Teubner.
Fuchs, H., Hofmann, F., Löhr, H., & Schaaf, G. (2015). Car-2-x. In H. Winner, S. Hakuli, F. Lotz, & C. Singer (Eds.), Handbuch fahrerassistenzsysteme (pp. 525–540). Vieweg + Teubner.
Gasser, T. M. (2012). Rechtsfolgen zunehmender fahrzeugautomatisierung: Gemein- samer schlussbericht der projektgruppe. Berichte der Bundesanstalt für Strassen- wesen. Unterreihe Fahrzeugtechnik(83).
Gasser, T. M. (2013). Herausforderungen automatischen fahrens und forschungsschw- erpunkte. 6. Tagung Fahrerassistenzsysteme. Munich, Germany, 6.
Gasser, T. M., & Westhoff, D. (2012). Bast-study definitions of automation and legal issues in germany. TRB Road Vehicle Automation Workshop.
Gazis, D. C., Herman, R., & Rothery, R. W. (1961). Nonlinear follow-the-leader models of traffic flow. Operations Research, 9(4), 545–567.
Geddes, N. B. (1940). Magic motorways. New York: Random House.
Geiger, A., Lauer, M., Moosmann, F., Ranft, B., Rapp, H., Stiller, C., & Ziegler, J. (2012). Team annieway’s entry to the 2011 grand cooperative driving challenge. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1008–1017.
Geiser, G. (1985). Mensch-maschine-kommunikation im kraftfahrzeug. ATZextra, 87(2), 77–84.
Geyer, S., Baltzer, M., Franz, B., Hakuli, S., Kauer, M., Kienle, M., . . . Winner, H. (2014). Concept and development of a unified ontology for generating test and use-case catalogues for assisted and automated vehicle guidance. IET Intelligent Transport Systems, 8(3), 183–189.
Gindele, T., Brechtel, S., & Dillmann, R. (2010). A probabilistic model for estimating driver behaviors and vehicle trajectories in traffic environments. Intelligent Transportation Systems (ITSC), 13, 1625–1631.
Gipps, P. (1981). A behavioural car-following model for computer simulation. Transporta- tion Research Part B: Methodological, 15(2), 105–111.
Glaser, S., Vanholme, B., Mammar, S., Gruyer, D., & Nouveliere, L. (2010). Maneuver- based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction. IEEE Transactions on Intelligent Transportation Systems, 11(3), 589–606.
Gold, C., & Bengler, K. (2014). Taking over control from highly automated vehicles. Advances in Human Aspects of Transportation: Part II, 8, 64–69.
Gold, C., Berisha, I., & Bengler, K. (2015). Utilization of drivetime - performing non-driving related tasks while driving highly automated. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59(1), 1666–1670.
Gold, C., Dambock, D., Lorenz, L., & Bengler, K. (2013). Take over! how long does it take to get the driver back into the loop? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 1938–1942.
Gold, C., Körber, M., Hohenberger, C., Lechner, D., & Bengler, K. (2015). Trust in automation – before and after the experience of take-over scenarios in a highly automated vehicle. 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, 3, 3025– 3032.
Gold, C., Körber, M., Lechner, D., & Bengler, K. (2016). Taking over control from highly automated vehicles in complex traffic situations: The role of traffic density. Human Factors: The Journal of the Human Factors and Ergonomics Society.
Gold, C., Lorenz, L., & Bengler, K. (2014). Influence of automated brake application on take-over situations in highly automated driving scenarios. Proceedings of FISITA World Automotive Congress. Maastricht, Netherlands.
Gold, C., Lorenz, L., Damböck, D., & Bengler, K. (2013). Partially automated driving as a fallback level of high automation. 6. Tagung Fahrerassistenzsysteme. Munich, Germany, 6.
Goto, Y., & Stentz, A. (1987). The cmu system for mobile robot navigation. IEEE International Conference on Robotics and Automation (ICRA), 4, 99–105.
Green, D. M., & Gierke, S. M. v. (1984). Visual and auditory choice reaction times. Acta Psychologica, 55(3), 231–247.
Guang Lu, & Tomizuka, M. (2002). Vehicle lateral control with combined use of a laser scanning radar sensor and rear magnetometers. Proceedings of the 2002 American Control Conference, 5, 3702–3707.
Günthner, S., Schmid, B., Stählin, U., & Jürgens, G. (2014). Sensorsysteme für car2x: Herausforderungen für die fahrzeugarchitektur. Tagungsband 30. VDI/VW Gemein- schaftstagung, 30.
Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Scoffier, M., Kavukcuoglu, K., . . . LeCun, Y. (2009). Learning long-range vision for autonomous off-road driving. Journal of Field Robotics, Special Issue on LAGR Program, Part II, 26(2), 120–144.
Happee, R., Gold, C., Radlmayr, J., Hergeth, S., & Bengler, K. (2016). Take-over performance in evasive manoeuvres. Manuscript submitted for publication.
Harrell, F. E. (2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer New York.
Hergeth, S., Lorenz, L., & Krems, J. (2016). What did you expect? effects of prior familiarization with take-over requests during conditional automated driving on take- over performance and automation trust. Manuscript submitted for publication.
Herslund, M.-B., & Jørgensen, N. O. (2003). Looked-but-failed-to-see-errors in traffic. Accident Analysis & Prevention, 35(6), 885–891.
Hoeger,R.,Amditis,A.,Kunert,M.,Hoess,A.,Flemisch,F.,Krüger,H.-P.,... Beutner,A. (2008). Highly automated vehicles for intelligent transport: Haveit approach. ITS World Congress. New York, United States.
Hoffmann, J. (2008). Das darmstädter verfahren (evita) zum testen und bewerten von frontalkollisionsgegenmaßnahmen (doctoral dissertation). Darmstadt: Technische Universität Darmstadt, Fachbereich Maschinenbau, Darmstadt, Germany.
Höhne, P. (1974). Die abhängigkeit der einfachen visuellen psychomotorischen reaktion- szeit des menschen von verschiedenen parametern (doctoral dissertation). Freie Universität Berlin, Berlin, Germany.
ISO. (11.2012). Road vehicles - ergonomic aspects of transport information and control systems - calibration tasks for methods which assess driver demand due to the use of in-vehicle systems (No. ISO/TS 14198). ISO International Organization for Standardization.
ISO International Organization for Standardization. (2013-07-23). Intelligent transport systems – forward vehicle collision warning systems – performance requirements and test procedures (Vol. 03.220.01; 35.240.60; 43.040.99) (No. 15623:2013). ISO International Organization for Standardization.
Itoh, M., Horikome, T., & Inagaki, T. (2013). Effectiveness and driver acceptance of a semi- autonomous forward obstacle collision avoidance system. Applied Ergonomics, 44(5), 756–763.
Jamson, A. H., Merat, N., Carsten, O. M., & Lai, F. C. (2013). Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions. Transportation Research Part C: Emerging Technologies, 30, 116–125.
Kahneman, D. (1973). Attention and effort. Englewood Cliffs and N.J: Prentice-Hall. Kammel, S., & Pitzer, B. (2008). Lidar-based lane marker detection and mapping. IEEE
Intelligent Vehicles Symposium (IV), 1137–1142.
Kerschbaum, P., Lorenz, L., & Bengler, K. (2014). Highly automated driving with a decoupled steering wheel. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 1686–1690.
Kerschbaum, P., Lorenz, L., & Bengler, K. (2015). A transforming steering wheel for highly automated cars. IEEE Intelligent Vehicles Symposium (IV), 1287–1292.
Keuss, P. (1972). Reaction time to the second of two shortly spaced auditory signals both varying in intensity. Acta Psychologica, 36(3), 226–238.
Kiefer, R. J., LeBlanc, D. J., & Flannagan, C. A. (2005). Developing an inverse time- to-collision crash alert timing approach based on drivers’ last-second braking and steering judgments. Accident Analysis & Prevention, 37(2), 295–303.
Kienle, M. (2015). Kooperative interaktionskonzepte zum fahren eines teilautoma- tisierten fahrzeugs (doctoral dissertation). München: Technical University of Mu- nich, Institute of Ergonomics, Munich, Germany.
Kim, K. W., & Kim, D. (2011). Development of a neural network for the estimation of drivers’ route choice. International Journal of Urban Sciences, 8(2), 131–145.
Kimoto, K., & Thorpe, C. (1998). Map building with radar and motion sensors for automated highway vehicle navigation. Proceedings of the 1997 IEEE/RSJ Inter- national Conference on Intelligent Robots and Systems(3), 1721–1728.
Klein, S. B. (1982). Motivation: Biosocial approaches. New York: McGraw-Hill.
Körber, M., & Bengler, K. (2014). Potential individual differences regarding automation effects in automated driving. Proceedings of the XV International Conference on Human Computer Interaction (ACM), 1–7.
Körber, M., Gold, C., Lechner, D., & Bengler, K. (2016). The influence of age on the take-over of vehicle control in highly automated driving. Transportation Research Part F: Traffic Psychology and Behaviour, 39, 19–32.
Kovácˇ, D. (1969). From reaction time to the measuring of promptness. Acta Psychologica, 30, 75–83.
Kozak, A., & Kozak, R. (2003). Does cross validation provide additional information in the evaluation of regression models? Canadian Journal of Forest Research, 33(6), 976–987.
Kramer, A. F., Cassavaugh, N., Horrey, W. J., Becic, E., & Mayhugh, J. L. (2007). Influence of age and proximity warning devices on collision avoidance in simulated driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49(5), 935–949.
Kray, J., & Lindenberger, U. (2000). Adult age differences in task switching. Psychology and Aging, 15(1), 126–147.
Krinchik, E. (1969). The probability of a signal as a determinant of reaction time. Acta Psychologica, 30, 27–36.
Kühne, R., Aoki, R., Kellermann, G., Manstetten, D., Schnittger, S., & Wu, N. (2004). Fgsv merkblatt (entwurf): Das fundamentaldiagramm - grundlagen und anwendungen. Berlin.
Lange, A., Maas, M., Albert, M., Siedersberger, K.-H., & Bengler, K. (2014). Automa- tisiertes fahren - so komfortabel wie möglich, so dynamisch wie nötig. verstibuläre zustandsrückmeldung beim automatisierten fahren. 30. VDI/VW Gemeinschaftsta- gung Fahrerassistenz und Integrierte Sicherheit. Wolfsburg, Germany, 30.
Langenberg, J., Bartels, A., & Etemad, A. (2014). Eu-projekt adaptive: Ansätze für hochautomatisches fahren. 30. VDI/VW Gemeinschaftstagung Fahrerassistenz und Integrierte Sicherheit. Wolfsburg, Germany, 30.
Larsson, A. (2010). Issues in reclaiming control from advanced driver assistance sys- tems. European Conference on Human Centred Design for Intelligent Transport Systems, 2, 557–564.
Lechner, D. (2015). Einfluss altersbedingter effekte auf übernahmezeit und –qualität beim hochautomatisierten fahren (bachelor’s thesis). Technical University of Mu- nich, Institute of Ergonomics, Munich, Germany.
Lee, D. N. (1976). A theory of visual control of braking based on information about time-to-collision. Perceptions, 5, 437–459.
Levitan, L., Golembiewski, G., & Bloomfield, J. R. (1998). Human factors issues for automated highway systems. ITS Journal - Intelligent Transportation Systems Journal, 4(1-2), 21–47.
Lindner, P., & Wanielik, G. (2009). 3d lidar processing for vehicle safety and environ- ment recognition. IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, 66–71.
Liu, Y.-T., Lin, Y.-Y., Wu, S.-L., Chuang, C.-H., Prasad, M., & Lin, C.-T. (2014). Eeg-based driving fatigue prediction system using functional-link-based fuzzy neural network. International Joint Conference on Neural Networks, 4109–4113.
Lorenz, L., Hergeth, S., Kerschbaum, P., Gold, C., & Radlmayr, J. (2015). Der fahrer im hochautomatisierten fahrzeug. vom dual-task zum sequential-task paradigma: Ein rückblick über fahrsimulatorstudien. 7. Tagung Fahrerassistez. Munich, Germany, 7.
Lorenz, L., Kerschbaum, P., & Schumann, J. (2014). Designing take over scenarios for automated driving: How does augmented reality support the driver to get back into the loop? Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 1681–1685.
Louw, T., Merat, N., & Jamson, A. H. (2015). Engaging with highly automated driving: To be or not to be in the loop? 8th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design. Salt Lake City, USA, 8.
Lu, G., & Tomizuka, M. (2006). Lidar sensing for vehicle lateral guidance: Algorithm and experimental study. IEEE/ASME Transactions on Mechatronics, 11(6), 653–660.
Makishita, H., & Matsunaga, K. (2008). Differences of drivers’ reaction times according to age and mental workload. Accident Analysis & Prevention, 40(2), 567–575.
Mark, van der W., & Gavrila, D. (2006). Real-time dense stereo for intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems, 7(1), 38–50.
Markkula, G., Benderius, O., Wolff, K., & Wahde, M. (2012). A review of near-collision driver behavior models. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(6), 1117–1143.
Markoff, J. (2010). Google cars drive themselves, in traffic. The New York Times, 10(A1), 9.
Marsden, G., McDonald, M., & Brackstone, M. (2001). Towards an understanding of adaptive cruise control. Transportation Research Part C: Emerging Technologies, 9(1), 33–51.
Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268.
Maurer, M., Behringer, R., Furst, S., Thomanek, F., & Dickmanns, E. (1996). A compact vision system for road vehicle guidance. Proceedings of the 13th International Conference on Pattern Recognition, 13(3), 313–317.
McGehee, D. V., Mazzae, E. N., & Baldwin, G. H. S. (2000). Driver reaction time in crash avoidance research: Validation of a driving simulator study on a test track. Pro- ceedings of the Human Factors and Ergonomics Society Annual Meeting, 44(20), 3-320–3-323.
Merat, N., & Jamson, A. H. (2009). How do drivers behave in a highly automated car? Proceedings of the 5th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 5, 514–521.
Merat, N., Jamson, A. H., Lai, F. C. H., & Carsten, O. (2012). Highly automated driving, secondary task performance, and driver state. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(5), 762–771.
Merat, N., Jamson, A. H., Lai, F. C. H., Daly, M., & Carsten, O. M. (2014). Transition to manual: Driver behaviour when resuming control from a highly automated vehicle. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 274–282.
Mercedes-Benz. (2015). Interaktive betriebsanleitung s-klasse. Retrieved from: http://moba.i.daimler.com/baixn/cars/222/de_DE/index.html#../../../cars/222/de _DE /manual/: Author.
Montgomery, D. C., & Peck, E. A. (1992). Introduction to linear regression analysis (2nd ed ed.). New York: Wiley.
Moon, Y.-G., Kim, M.-S., & Lee, M.-C. (2012). Development of autonomous vehicle control algorithm based on dgps(rtk) and test vehicle performance verification. 12th International Conference on Control, Automation and Systems (ICCAS), 12, 1644–1648.
Moras, J., Cherfaoui, V., & Bonnifait, P. (2010). A lidar perception scheme for intelligent vehicle navigation. 11th International Conference on Control Automation Robotics & Vision (ICARCV), 11, 1809–1814.
Moravec, H. P. (1980). Obstacle avoidance and navigation in the real world by a see- ing robot rover (No. STAN-CD-80-813). Stanford: Stanford Artificial Intelligence Laboratory.
Moray, N. (1967). Where is capacity limited? a survey and a model. Acta Psychologica, 27, 84–92.
Moray, N. (1979). Mental workload: Its theory and measurement (Vol. 8). Boston and MA: Springer.
Najm, W. G., Smith, J. D., & Yanagisawa, M. (2007). Pre-crash scenario typology for crash avoidance research (No. DOT HS 810 767).
National Highway Traffic Safety Administration. (2008). National motor vehicle crash causation survey: Report to congress (No. DOT HS 811 059). Springfield and Virginia: National Technical Information Service.
Naujoks, F., Mai, C., & Neukum, A. (2014). The effect of urgency of take-over requests during highly automated driving under distraction conditions. Advances in Human Aspects of Transportation Part I, 431–438.
Negele, J. (2007). Anwendungsgerechte konzipierung von fahrsimulatoren für die fahrzeugentwicklung (doctoral dissertation). Technische Universität München, Lehrstuhl für Fahrzeugtechnik, Munich, Germany.
Neubauer, C., Matthews, G., & Saxby, D. (2012). The effects of cell phone use and automation on driver performance and subjective state in simulated driving. Pro- ceedings of the Human Factors and Ergonomics Society Annual Meeting, 56(1), 1987–1991.
New York Times. (1925). Houdini subpoenaed waiting to broadcast: Magician must appear in court on charge that he was disorderly in plaintiff’s office. The New York Times, July 23.
NHTSA. (2013). Preliminary statement of policy concerning automated ve- hicles. Retrieved from: www.nhtsa.gov/ staticfiles/ rulemaking/ pdf/ Auto- mated_Vehicles_Policy.pdf: National Highway Traffic Safety Administration.
Nieuwenhuis, S., & Monsell, S. (2002). Residual costs in task switching: Testing the failure-to-engage hypothesis. Psychonomic Bulletin & Review, 9(1), 86–92.
Niknejad, H. T., Takahashi, K., Mita, S., & McAllester, D. (2011). Embedded multi-sensors objects detection and tracking for urban autonomous driving. Intelligent Vehicles Symposium (IV), 1128–1135.
Othersen, I., Petermann-Stock, I., & Vollrath, M. (2014). Bitte überwachen! - eine analyse des teilautomatisierten fahrens. 30. VDI/VW Gemeinschaftstagung Fahrerassis- tenz und Integrierte Sicherheit. Wolfsburg, Germany, 30.
Panwai, S., & Dia, H. (2007). Neural agent car-following models. IEEE Transactions on Intelligent Transportation Systems, 8(1), 60–70.
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors: The Journal of the Human Factors and Ergonomics Society, 52(3), 381–410.
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, dis- use, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230–253.
Pashler, H. (2000). Task switching and multitask performance. In S. Monsell & J. Driver (Eds.), Control of cognitive processes (pp. 277–307). MIT Press.
Patten, C. J., Kircher, A., Östlund, J., & Nilsson, L. (2004). Using mobile telephones: cog- nitive workload and attention resource allocation. Accident Analysis & Prevention, 36(3), 341–350.
Petermann, I., & Kiss, M. (2010). Die rolle des fahrers im spektrum von automation und transition: Nutzerzentrierte gestaltung von übergabe- und übernahmeprozessen zwischen fahrer und drei aufeinander aufbauenden automationsstufen. ATZextra, 15(2), 90–95.
Petermann, I., & Schlag, B. (2010). Auswirkungen der synthese von assistenz und automation auf das fahrer-fahrzeug system. Proceedings of the AAET .
Petermann-Stock, I., Hackenberg, L., Muhr, T., & Mergl, C. (2013). Wie lange braucht der fahrer? eine analyse zu übernahmezeiten aus verschiedenen nebentätigkeiten während einer hochautomatisierten staufahrt. 6. Tagung Fahrerassistenzsysteme. Munich, Germany, 6.
Petermeijer, S., Winter, J. C. d., & Bengler, K. (2015). Vibrotactile displays: A survey with a view on highly automated driving. IEEE Transactions on Intelligent Transportation Systems, 1–11.
Pötzinger, C. (2013). Hochautomatisiertes fahren - bewertung definierter systemfunk- tionen und resultierender fahrerreaktionen (master’s thesis). Technical University of Munich, Institute of Ergonomics, Munich, Germany.
Prakash, A. . K., Patil, A., & Kalyani, U. (2013). Artificial neural network based driver modeling for vehicle systems (No. 2013-01-2860). SAE International.
Preisinger, I. (2013). Daimler aims to launch self-driving car by 2020. Retrieved from: http://www.reuters.com/article/us-autoshow-frankfurt-daimler- selfdrive-idUSBRE98709A20130908: Reuters.
Radlmayr, J. (2013). Einfluss von verkehrssituation und aufgabe auf übernahmezeit und -qualität beim hochautomatisierten fahren (master’s thesis). Technical University of Munich, Institute of Ergonomics, Munich, Germany.
Radlmayr, J., Gold, C., Lorenz, L., Farid, M., & Bengler, K. (2014). How traffic situations and non-driving related tasks affect the take-over quality in highly automated driv- ing. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 2063–2067.
Ranft, B., & Strauß, T. (2014). Hocheffizientes stereosehen durch modellierung geneigter ebenen und parallelverarbeitung. In B. Färber & Uni-DAS e.V. (Eds.), 9. workshop fahrerassistenzsysteme (fas2014) (pp. 27–36). Uni-DAS e.V.
Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Transactions on Systems, Man, and Cybernetics, 13(3), 257–266.
Rauch, N. (2009). Ein verhaltensbasiertes messmodell zur erfassung von situations- bewusstsein im fahrkontext (doctoral dissertation). Psychologisches Institut der Universität Würzburg, Würzburg, Germany.
Reason, J. T. (1990). Human error. Cambridge: Cambridge University Press.
Reichenbach, J., Onnasch, L., & Manzey, D. (2010). Misuse of automation: The impact of system experience on complacency and automation bias in interaction with automated aids. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 54(4), 374–378.
Reimer, B., Mehler, B., Wang, Y., & Coughlin, J. F. (2010). The impact of system- atic variation of cognitive demand on drivers’ visual attention across multiple age groups. Proceedings of the Human Factors and Ergonomics Society Annual Meet- ing, 54(24), 2052–2055.
Rojo, J., Rojas, & Raul. (2007). Spirit of berlin: An autonomous car for the darpa urban challenge hardware and software architecture. Freie Universität Berlin.
SAE International. (2014). Sae surface vehicle information report: Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems (No. J3016). SAE International.
Salthouse, T. A. (1991). Mediation of adult age differences in cognition by reductions in working memory and speed of processing. Psychological Science, 2(3), 179–183.
Salvucci, D. D., & Liu, A. (2002). The time course of a lane change: Driver control and eye-movement behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 5(2), 123–132.
Sarter, N. B., & Woods, D. D. (1995). How in the world did we get into that mode? mode error and awareness in supervisory control. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 5–19.
Sayed, R., & Eskandarian, A. (2001). Unobtrusive drowsiness detection by neural network learning of driver steering. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 215(9), 969–975.
Schefter, J. (1985). Look, ma! no driver. Popular Science, 227(HS-039 355), 4. Schlick, C., Bruder, R., & Luczak, H. (2010). Arbeitswissenschaft (3., überarb. und erw.
Aufl ed.). Berlin and Heidelberg: Springer-Verlag Berlin Heidelberg.
Schmidtke, H. (1961). Der einfluss der reizintensität auf die reaktionszeit. Psychologische
Beiträge, 6.
Schmidtke, H., & Micko, H. (1964). Untersuchungen über die reaktionszeit bei dauer-
beobachtung. Köln und Opladen: Westdeutschler Verlag.
Schöpplein, S. E. (2013). Integration fahrstreifenbezogener kenngrößen und seitlicher detektionsdaten in ein makroskopisches verkehrsflussmodell für dreistreifige rich- tungsfahrbahnen (doctoral dissertation). München: Technische Universität München, Lehrstuhl für Verkehrstechnik – Institut für Verkehrswesen, Munich, Germany.
Schreiner, O. (2014). Holistic hmi solutions towards automated driving. Vehicle Interac- tion Summit 2014 - Fahrermodelle für Automatisiertes Fahren.
Schweigert, M. (2003). Fahrerblickverhalten und nebenaufgaben (doctoral dissertation). Technical University of Munich, Institute of Ergonomics, Munich, Germany.
Scott, J. J., & Gray, R. (2008). A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(2), 264–275.
Shen, S., & Neyens, D. M. (2014). Assessing drivers’ performance when automated driver support systems fail with different levels of automation. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58(1), 2068–2072.
Shladover, S. (2006). Path at 20 - history and major milestones. IEEE Transactions on Intelligent Transportation Systems, 8(4), 584–592.
Snijders, T. A. (2005). Power and sample size in multilevel modeling. Encyclopedia of Statistics in Behavioral Science(3), 1570–1573.
Soyer, G. (2013). Untersuchung von einflüssen automatischer bremsmanöver und verkehrssituationen auf die übernahmezeit und -qualität in hochautomatisierten fahrzeugen (master’s thesis). Technical University of Munich, Institute of Er- gonomics, Munich, Germany.
Stanton, N. A., & Marsden, P. (1996). From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles. Safety Science, 24(1), 35–49.
Stanton, N. A., Young, M., & McCaulder, B. (1997). Drive-by-wire: The case of driver workload and reclaiming control with adaptive cruise control. Safety Science, 27(2-3), 149–159.
Stanton, N. A., Young, M. S., Walker, G. H., Turner, H., & Randle, S. (2001). Automating the driver’s control tasks. International Journal of Cognitive Ergonomics, 5(3), 221–236.
Statistisches Bundesamt. (2015). Verkehrsunfälle - fachserie 8 reihe 7 - 2014 (No. 2080700147004). Statistisches Bundesamt.
Statistisches Bundesamt, Wiesbaden. (2012). Verkehrsunfälle: Unfälle von senioren im straßenverkehr 2011 (Vol. 546240911900-4). Wiesbaden.
Strand, N., Nilsson, J., Karlsson, I. M., & Nilsson, L. (2014). Semi-automated ver- sus highly automated driving in critical situations caused by automation failures. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 218–228.
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293.
Teigen, K. H. (1994). Yerkes-dodson: A law for all seasons. Theory & Psychology, 4(4), 525–547.
Thorpe, C., Herbert, M., Kanade, T., & Shafer, S. (1991). Toward autonomous driving: the cmu navlab. i. perception. IEEE Expert, 6(4), 31–42.
Toffetti, A., Wilschut, E., Martens, M., Schieben, A., Rambaldini, A., Merat, N., & Flemisch, F. (2009). Citymobil: Human factor issues regarding highly-automated vehicles on an elane. Transportation Research Record: Journal of the Transportation Re- search Board, 2110, 1–8.
Treat, J., Tumbas, N., McDonald, S., Shinar, D., Hume, R., Mayer, R., . . . Castellan, N. (1979). Tri-level study of the causes of traffic accidents: Executive summary (No. DOT HS-034-3-535). U.S. Department of Transportation.
van Dijke, J., & van Schijndel, M. (2012). Citymobil, advanced transport for the ur- ban environment. Transportation Research Record: Journal of the Transportation Research Board, 2324, 29–36.
Velodyne Scoustics Inc. (2014). Data sheet - high definition lidar hdl-64e (No. 63-9194 Rev B). Retrieved from: http://www.velodynelidar.com/lidar/products/brochure/HDL- 64E%20Data%20Sheet.pdf: Velodyne Scoustics Inc.
Waard, D. d., Hulst, M. v. d., Hoedemaeker, M., & Brookhuis, K. A. (1999). Driver behavior in an emergency situation in the automated highway system. Transportation human factors, 1(1), 67–82.
Wada, T., Doi, S., Imai, K., Tsuru, N., Isaji, K., & Kaneko, H. (2007). On driver’s braking behavior in car following. SICE, 2007 Annual Conference, 2396–2401.
Wahab, A., Chai Quek, Chin Keong Tan, & Takeda, K. (2009). Driving profile modeling and recognition based on soft computing approach. IEEE Transactions on Neural Networks, 20(4), 563–582.
Warm, J. S., Parasuraman, R., & Matthews, G. (2008). Vigilance requires hard mental work and is stressful. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 433–441.
Wei, H., Ross, W., Varisco, S., Krief, P., & Ferrari, S. (2013). Modeling of human driver behavior via receding horizon and artificial neural network controllers. 52nd IEEE Conference on Decision and Control, 52, 6778–6785.
Welford, A., Brebner, J., & Kirby, N. (1980). Reaction times. London and New York and Toronto: Academic Press.
Weller, G., & Schlag, B. (2009). A robust method to detect driver distraction. Pro- ceedings European Conference Human Centered Design Intelligent Transporta- tion Systems, 279–288.
Wenger, J. (2005). Automotive radar - status and perspectives. IEEE Compound Semiconductor Integrated Circuit Symposium.
Wickens, C. D. (2008a). Multiple resources and mental workload. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 449–455.
Wickens, C. D. (2008b). Situation awareness: Review of mica endsley’s 1995 articles on situation awareness theory and measurement. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50(3), 397–403.
Wickens, C. D., Hollands, J. G., Banbury, S., & Parasuraman, R. (2013). Engineering psychology and human performance (Fourth edition ed.). New York: Routledge.
Wille, J. M., Saust, F., & Maurer, M. (2010). Stadtpilot: Driving autonomously on braunschweig’s inner ring road. IEEE Intelligent Vehicles Symposium (IV), 506– 511.
Williams, M., & Preston, N. C. (1987). The prometheus project. 6th International Conference on Automotive Electronics, 6(280), 36–39.
Winner, H., & Hakuli, S. (2015). Handbook of driver assistance systems: Basic infor- mation, components and systems for active safety and comfort (2., korr. Aufl ed.). Wiesbaden: Springer Verlag.
Winner, H., & Wachenfeld, W. (2013). Absicherung automatischen fahrens. 6. Tagung Fahrerassistenzsysteme. Munich, Germany.
Winter, J. C. d., Happee, R., Martens, M. H., & Stanton, N. A. (2014). Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence. Transportation Research Part F: Traffic Psychology and Behaviour, 27, 196–217.
Xie, S. (2014). Einfluss von verkehrsdichte und verbaler kommunikation auf das über- nahmeverhalten im hochautomatisierten fahrzeug (master’s thesis). Technical Uni- versity of Munich, Institute of Ergonomics, Munich, Germany.
Yamaguchi, K., Kato, T., & Ninomiya, Y. (2006). Vehicle ego-motion estimation and moving object detection using a monocular camera. 18th International Conference on Pattern Recognition, ICPR(4), 610–613.
Yan, X., Radwan, E., & Abdel-Aty, M. (2005). Characteristics of rear-end accidents at signalized intersections using multiple logistic regression model. Accident Analysis & Prevention, 37(6), 983–995.
Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit–formation. Journal of comparative neurology and psychology, 18(5), 459– 482.
Young, M. S., & Stanton, N. A. (1997). Automotive automation: Investigating the impact on driver mental workload. International Journal of Cognitive Ergonomics, 1(4), 325–336.
Young, M. S., & Stanton, N. A. (2007a). Back to the future: Brake reaction times for manual and automated vehicles. Ergonomics, 50(1), 46–58.
Young, M. S., & Stanton, N. A. (2007b). What’s skill got to do with it? vehicle automation and driver mental workload. Ergonomics, 50(8), 1324–1339.
Zeeb, K., Buchner, A., & Schrauf, M. (2015). What determines the take-over time? an integrated model approach of driver take-over after automated driving. Accident Analysis & Prevention, 78, 212–221.
Zehang, S., Miller, R., Bebis, G., & DiMeo, D. (2002). A real-time precrash vehicle de- tection system. Sixth IEEE Workshop on Applications of Computer Vision (WACV 2002), 171–176.
ZENTEC GmbH. (2015). Start des bmwi leuchtturmprojekts ko-haf. Re- trieved from: http://www.zentec.de/presse/newsdetailseite/article/start-des-bmwi- leuchtturmprojekts-ko-haf-kooperatives-hochautomatisiertes-fahren.html: ZENTEC GmbH.
Zhang, S., Deng, W., Zhao, Q., Sun, H., & Litkouhi, B. (2013). Dynamic trajectory planning for vehicle autonomous driving. IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4161–4166.
Zhang, W. (2010). Lidar-based road and road-edge detection. Intelligent Vehicles Symposium (IV), 845–848.
Zimmermann, M., & Bengler, K. (2013). A multimodal interaction concept for cooperative driving. IEEE Intelligent Vehicles Symposium (IV), 1285–1290.

[20]

Bainbridge, L. (1983). Ironies of Automation. Automatica, 19(6), 775–779.
Chiellino, U., Gail, J., Hoppe, M., Jaensch, M., Liers, H., Nehmzow, J., Otte, D., Pund, B (2007). Results of the AARU, VW & GIDAS Task force Active Safety.
Damböck, D., Farid, M., Tönert, L., Bengler, K. (2012a). Übernahmezeiten beim hochautomatisierten Autofahren . 5. Tagung Fahrerassistenz 2012. München. Germany.
Damböck, D., Weißgerber, T., Kienle, M., Bengler, K. (2012b). Evaluation of a Contact Analog Head-Up Display for Highly Automated Driving. 4th International Conference on Applied Human Factors and Ergonomics. San Francisco. USA.
Endsley, M. R. : Toward a theory of situation awareness. Human Factors, 37, 32-64. (1995)
Endsley, M. R., Kiris, E. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors, 37, 381-394.
Flemisch, F. O., Lorenz, B., Oberheid, H., Brookhuis, K. A. (Eds.) in de Waard, D. (2008). Human Factors for assistance and automation (pp. 1-16). Maastricht, the Netherlands: Shaker Publishing.
Gasser, T. M., Arzt, C., Ayoubi, M., Bartels, A., Eier, J., Flemisch, F., Häcker, D., Hesse, T., Huber, W., Lotz, C., Maurer, M., Ruth-Schumacher, S., Schwarz, J., Vogt, W. (2012). BASt-Bericht F 83: Rechtsfolgen zunehmender Fahrzeugautomatisierung. Bremerhaven.
Hancock, P.A., Simmons, L., Hashemi, L., Howarth, H., Ranney, T. (1999). The Effects of In-Vehicle Distraction on Driver Response during a Crucial Driving Maneuver. Transportation Human Factors, 4:295-309.
ISO/TS 14198 (11.2012) . Road vehicles - Ergonomic aspects of transport information and control systems - Calibration tasks for methods which assess driver demand due to the use of in-vehicle systems: ISO International Organization for Standardization.
Lee, J. D., McGehee, D. V., Brown, T. L., Reyes, M. L. (2002). Collision Warning Timing, Driver Distraction, and Driver Response to Imminent Rear-End Collisions in a High-Fidelity Driving Simulator. Human Factors, 44(2), 314–334.
Google Scholar | SAGE Journals | ISI
Merat, N., Lee, J. D. (2012). Preface to the Special Section on Human Factors and Automation in Vehicles: Designing Highly Automated Vehicles With the Driver in Mind. Human Factors, 54(5), 681–686.
Pacejka, H. B. (2006). Tyre and Vehicle Dynamics (2nd ed.). Society of Automotive Engineers, Inc.. pp. 5.
Peterman, I., Kiss, M. (2009). Die Rolle des Fahrers im Spektrum von Automation und Transition, VDI-Berichte Nr. 2085; VDI Fahrer im 21. Jahrhundert, Braunschweig.
Sarter, N. B., Woods, D. D. (1995). How in the world did we ever get into that mode? Mode error and awareness in supervisory control. Human Factors, 37, 5-19.
####[21] References:
[1] developers.google.com/protocol-buffers/
Scientific publications and simulators supporting OSI:
T. Hanke, N. Hirsenkorn, B. Dehlink, A. Rauch, R. Rasshofer, and E. Biebl, “Generic architecture for simulation of ADAS sensors,” in International Radar Symposium, pp. 125–130, IEEE, 2015.
N. Hirsenkorn, T. Hanke, A. Rauch, B. Dehlink, R. Rasshofer, and E. Biebl, “A non-parametric approach for modeling sensor behavior,” in International Radar Symposium, pp. 131–136, DGON, 2015.
N. Hirsenkorn, T. Hanke, A. Rauch, B. Dehlink, R. Rasshofer, and E. Biebl, “Virtual sensor models for real-time applications,” Advances in Radio Science, vol. 14, pp. 31–37, 2016.
T. Hanke, N. Hirsenkorn, B. Dehlink, A. Rauch, R. Rasshofer, and E. Biebl, “Classification of Sensor Errors for the Statistical Simulation of Environmental Perception in Automated Driving Systems,” in International Conference on Intelligent Transportation Systems, IEEE, 2016.
N. Hirsenkorn, H. Kolsi, M. Selmi, A. Schaermann, T. Hanke, A. Rauch, R. Rasshofer, and E. Biebl, “Learning Sensor Models for Virtual Test and Development,” Workshop Fahrerassistenz und automatisiertes Fahren, vol. 11, 2017.
N. Hirsenkorn, P. Subkowski, T. Hanke, A. Schaermann, A. Rauch, R. Rasshofer, and E. Biebl, “A Ray Launching Approach for Modeling an FMCW Radar System”, in International Radar Symposium, DGON, 2017, accepted.
A. Schaermann, A. Rauch, N. Hirsenkorn, T. Hanke, R. Rasshofer and E. Biebl, ”Validation of Virtual Perceptual Sensor Models,” in Intelligent Vehicles Symposium, IEEE, 2017, accepted.
T. Hanke, A. Schaermann, M. Geiger, K. Weiler, N. Hirsenkorn, S. Schneider and Erwin Biebl, ”Generation and Validation of Virtual Point Cloud Data for Automated Driving Systems,” in International Conference on Intelligent Transportation Systems, IEEE, 2017, submitted.

"SAFETY FIRST FOR AUTOMATED DRIVING" に追加するとよいかもしれないこと