大規模言語モデル講座 基礎編 2025 Autumn 敗因を勝因に
https://qiita.com/kaizen_nagoya/items/34ffd2b0c47a5f3665d9
はじめてのHugging Face with ChatGPT
https://qiita.com/kaizen_nagoya/items/2bd8d603acc09517e34a
ChatGPT
What is Hugging Face?
Hugging Face is a company and open-source community focused on natural language processing (NLP) and AI tools, especially for working with transformer-based models like BERT, GPT, and T5. Over time, it has become a central hub for machine learning models, datasets, and development tools. Here’s a breakdown:
- Model Hub
A repository of pretrained AI models for NLP, computer vision, and audio tasks.
Examples: text classification, translation, summarization, image recognition, speech-to-text.
Users can download and use models directly with a few lines of code.- Transformers Library
Open-source Python library that provides state-of-the-art transformer models.
Supports PyTorch, TensorFlow, and JAX.
Makes it easy to train, fine-tune, and deploy large language models (LLMs).- Datasets and Tokenizers
Hugging Face also hosts datasets for machine learning.
Provides fast tokenization tools for efficiently preparing text for models.- Inference and Deployment Tools
Hugging Face offers Inference API: run models in the cloud without managing infrastructure.
Tools for accelerated deployment, including spaces (for demos/web apps).- Community & Open Science
Hugging Face emphasizes open-source sharing and community collaboration.
Many models are contributed by researchers, companies, and enthusiasts.
💡 Analogy:
Hugging Face is like a “GitHub for AI models” — a place where you can find, share, and deploy AI models easily.
If you want, I can also explain how Hugging Face differs from other AI platforms like OpenAI and why it’s popular for both research and production. Do you want me to?
yes
Here’s a detailed comparison of Hugging Face vs OpenAI and similar AI platforms, highlighting why Hugging Face is unique:
- Focus and Philosophy
Feature Hugging Face OpenAI
Core Focus Open-source models, community collaboration, research-friendly tools Proprietary LLMs, commercial AI products
Philosophy “AI for everyone,” sharing models & datasets freely Controlled access to models, focus on safety & commercial use
Key point: Hugging Face emphasizes open science. Anyone can download, modify, or deploy models. OpenAI often provides cloud-hosted access to its models instead of open-source weights.- Access to Models
Feature Hugging Face OpenAI
Pretrained Models Thousands of open-source models across NLP, CV, audio Primarily GPT, Codex, and Whisper (closed-source for GPT)
Fine-Tuning Fully supported locally or via Hugging Face Hub Fine-tuning via API, but limited access to model internals
Deployment Run locally or via Hugging Face Inference API Cloud API only; local deployment often not possible
Key point: Hugging Face lets you run models offline if you want, which is crucial for privacy, customization, and edge applications.- Community & Ecosystem
Hugging Face has a large community of contributors, sharing models, datasets, and tutorials.
OpenAI focuses on providing its models as a service with curated examples.
Hugging Face encourages research reproducibility; OpenAI emphasizes polished products like ChatGPT.- Use Cases
Task Hugging Face OpenAI
Chatbots Build custom chatbots using open-source LLMs Use ChatGPT API
Research Experiment with model architectures, tokenizers, datasets Limited to API calls and GPT fine-tuning
Edge Deployment Yes (local inference possible) Mostly no (cloud API required)
Commercial Use Free or paid (depending on usage) Paid API usage- Summary
Hugging Face = Open, flexible, research-friendly. You can run, modify, and deploy models yourself.
OpenAI = Cloud-first, polished AI products. You mostly access models via API, with less control over internals.
💡 Analogy:
Hugging Face is like a toolbox and community for AI builders, while OpenAI is like a ready-to-use smart assistant you interact with via API.
If you want, I can also show a small example comparing Hugging Face vs OpenAI usage in code—like calling the same LLM locally vs via API. It really makes the difference clear. Do you want me to do that?
yes
Great! Let’s compare Hugging Face vs OpenAI using a simple text generation example. We’ll use Python for both.
- Using Hugging Face (Local / Open-Source Model)
# Install libraries first:
# pip install transformers torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Choose an open-source model
model_name = "gpt2"
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Prepare input
input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")
# Generate output
outputs = model.generate(inputs["input_ids"], max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
✅ Notes:
Runs locally on your machine.
You have full access to the model for fine-tuning, modifying, or integrating.
Works offline once the model is downloaded.
2. Using OpenAI (Cloud API)
# Install library:
# pip install openai
import openai
openai.api_key = "YOUR_API_KEY"
response = openai.Completion.create(
model="text-davinci-003",
prompt="Once upon a time",
max_tokens=50
)
print(response.choices[0].text.strip())
✅ Notes:
Runs on OpenAI servers, you cannot download the model.
Easy to use, minimal setup.
API usage is paid (based on tokens used).
Key Differences Highlighted by Code
Feature Hugging Face OpenAI
Model access Local & open-source Cloud-only, proprietary
Customization Full control, fine-tuning possible Limited to API parameters
Internet needed Only for initial download Always required
Cost Free (unless using hosted API) Paid API usage
If you want, I can make a diagram showing Hugging Face vs OpenAI workflow side by side—it makes the difference very visual. Do you want me to do that?
How can I read the hugging face open source?
If you want to read and explore Hugging Face open-source code, it depends on whether you mean their libraries (like Transformers, Datasets, Tokenizers, etc.) or models hosted on the Hub. Here’s a structured approach:
- Choose what to explore
Hugging Face has several open-source projects. The most popular are:
Transformers → https://github.com/huggingface/transformers
Pretrained models, model architectures, tokenization
Datasets → https://github.com/huggingface/datasets
Datasets library, data loading, preprocessing
Tokenizers → https://github.com/huggingface/tokenizers
Fast tokenization with Rust backend and Python bindings
Diffusers → https://github.com/huggingface/diffusers
For diffusion models (like Stable Diffusion)
Evaluate → https://github.com/huggingface/evaluate
Standardized metrics for model evaluation- Clone the repository
For example, to read the Transformers library:
git clone https://github.com/huggingface/transformers.git
cd transformers
This will download the full source code to your local machine.- Install dependencies (optional)
If you want to run or test the library while reading it:
pip install -e .
# or for full dev setup
pip install -e ".[dev]"
-e installs in “editable” mode, so you can modify code while using it.
".[dev]" installs extra dependencies for testing and development.
4. Read the code
Focus on main directories:
Directory What to read
src/transformers Core models and pipelines
tests Unit tests (good for understanding usage and edge cases)
examples Example scripts for training, inference, fine-tuning
docs Documentation and API reference
Tip: start with examples/pipelines or examples/text-classification to see practical usage, then trace back into the model code.
5. Optional: Explore models on the Hub
Some Hugging Face repositories only contain configuration + inference code; the actual pretrained weights live on the Hub:
Browse models: https://huggingface.co/models
Each model has files like config.json, pytorch_model.bin, and tokenizer.json that you can read or download.
You can load them in Python without installing the repo:
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")
6. Optional: Use local notebooks
Clone the repo
Open a Jupyter notebook inside it
Step through the code interactively (great for learning how a model processes inputs)
If you want, I can make a step-by-step plan for reading Hugging Face Transformers from zero, showing exactly which files to start with and how to trace the model code.
Do you want me to do that?
yes
reat! Here’s a step-by-step roadmap for reading the Hugging Face Transformers library from zero, structured so you can gradually understand how it works internally. I’ll focus on Python code, skipping Rust bindings for now.
Step 0: Set up your environment
Clone the repo:
git clone https://github.com/huggingface/transformers.git
cd transformers
Install dependencies in editable mode:
pip install -e .
pip install -e ".[dev]"
Optional: Install JupyterLab or VS Code for interactive exploration.
Step 1: Understand the entry point – pipelines
File: src/transformers/pipelines/init.py and src/transformers/pipelines/pipeline.py
Purpose: High-level interface for tasks like text classification, NER, translation, summarization.
Goal: See how pipeline("task") loads the model and tokenizer automatically.
Steps:
Open pipeline.py, look at Pipeline.init and Pipeline.call.
Trace how AutoModel and AutoTokenizer are used inside.
Example:
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
print(classifier("I love Hugging Face!"))
Then trace which classes pipeline uses internally (TextClassificationPipeline, etc.)
Step 2: Learn about Auto classes
Files: src/transformers/models/auto/
auto_model.py
auto_tokenizer.py
auto_config.py
Purpose: Automatically select the correct model/tokenizer class based on model name.
Steps:
Look at AutoModel.from_pretrained("bert-base-uncased").
See how it maps "bert" to BertModel internally.
Understand the factory pattern in Python.
Step 3: Explore Config
Files: src/transformers/configuration_utils.py and src/transformers/models/bert/configuration_bert.py
Purpose: Stores hyperparameters for models.
Steps:
See how BertConfig defines num_hidden_layers, hidden_size, etc.
Understand how configs are loaded from config.json on the Hub.
Tip: The config is passed to model class to build layers.
Step 4: Study the Model Classes
Folder: src/transformers/models//modeling_.py
Example: modeling_bert.py
Steps:
Start with BertModel → forward() function.
See how embeddings, encoder layers, and attention are connected.
Check forward() arguments (input_ids, attention_mask, etc.)
Optional: Draw a simple block diagram of how tokens flow through embeddings → encoder → output.
Step 5: Tokenizers
Folder: src/transformers/models//tokenization_.py
Example: tokenization_bert.py
Purpose: Converts text → input IDs for the model.
Steps:
Understand encode, batch_encode_plus, call methods.
Compare fast (tokenizers Rust backend) vs. slow Python tokenizer.
Step 6: Read examples
Folder: examples/
Pick a task, e.g., examples/text-classification/run_glue.py
Steps:
See how dataset is loaded using datasets library.
Look at Trainer API → handles training, evaluation, saving models.
Run a small example on a few samples to see input → model → output.
Step 7: Understand Utilities
Files: src/transformers/utils/ and src/transformers/file_utils.py
Purpose: Helpers for:
downloading models from Hugging Face Hub
caching
logging
Steps:
Look at hf_hub_download()
See how the library finds model files locally or downloads from Hub.
Step 8: Explore Tests
Folder: tests/
Purpose: Learn edge cases, usage, expected behavior.
Steps:
Pick a test for a model: tests/test_modeling_bert.py
See what inputs it uses and how outputs are verified.
Very useful for understanding what the code expects vs. produces.
Step 9: Optional – Diffusers / other models
Once you’re comfortable with Transformers, explore diffusers for generative models.
Steps are similar: pipeline → model → config → tokenizer → tests.
Tips for Reading
Use VS Code or PyCharm to jump to definitions easily.
Start with high-level pipeline → AutoModel → Config → Model.
Draw small flow diagrams: text → tokens → embeddings → layers → logits.
Run small interactive experiments with a few tokens to see intermediate outputs.
If you want, I can also make a diagram that visually maps all these connections from pipeline → tokenizer → config → model → output. It makes reading the repo way easier.
Do you want me to make that diagram?
Are there any vehicle model in the hugging face?
Yes — you’ll find vehicle‑related assets on the Hugging Face Hub, but there’s a bit of nuance: they are mostly datasets rather than full pre‑trained models of vehicles. Here are some examples and how to interpret them.
✅ Example vehicle‑datasets
The dataset DamianBoborzi/car_images: a large collection of car images with captions.
huggingface.co
The dataset dimensify/vehicles: labelled “Vehicle” and shows various vehicle 3D‑model or image inputs.
huggingface.co
The dataset DrBimmer/vehicle‑classification: real‑world image classification data of vehicles by type/manufacturer.
huggingface.co
⚠️ What you won’t always find
You may not find many (or any) pre‑trained deep models specifically built just for vehicle recognition, detection or modelling in the Hub’s main model library (though there may be some).
Many vehicle datasets are “image datasets of vehicles” rather than “models of vehicles”. If you want a model (for example: detect a car, classify its make/model, or generate a 3D‑vehicle model) you may need to fine‑tune an existing general computer vision model on a vehicle dataset.
🔍 What you can do
Browse the Hugging Face Model Hub and filter by task categories (e.g., “image‑classification”, “object‑detection”) and keyword “vehicle” or “car” to find pre‑trained models that are already fine‑tuned on vehicle‑related tasks.
If you find only datasets (like above), you can pick a general vision model (e.g., a ResNet/ViT or DETR or similar on the Hub) and fine‑tune it on the vehicle dataset for your application (vehicle detection, classification, etc.).
Ensure you check the dataset details: licenses, size, split, annotations, etc so you know what the dataset supports (classification, detection, segmentation).
If you need a 3D vehicle‑modeling (not just recognition), you may have to look beyond just Hugging Face and into more domain‑specific repositories (3D model repositories, CAD/Obj files) and integrate with a vision model.
If you like, I can search specifically on the Hugging Face Hub for pre‑trained models (not just datasets) that address “vehicle detection/classification” (e.g., cars, trucks) and list a few that you can use. Would you like me to do that?
I want to get the software architecture model of OSEK/OS, CAN, and Diag. What can i do.
Got it! You want a software architecture model — basically a high-level view of how OSEK/OS, CAN, and Diagnostic (UDS/Diag) software are structured, their interactions, and how to design or represent them. Here’s a systematic approach to get and build this:
- Understand each component first
A. OSEK/OS
Type: Real-time operating system standard for automotive ECUs.
Purpose: Task scheduling, alarms, events, resources, inter-task communication.
Key components:
Tasks: Basic/Extended
Alarms: Time-triggered events
Resources: Mutual exclusion
Events: Task synchronization
OS API: StartOS, ActivateTask, WaitEvent, etc.
B. CAN
Type: Controller Area Network, in-vehicle communication bus.
Purpose: Message-based communication between ECUs.
Key layers (AUTOSAR-like mapping):
CAN Driver: Hardware abstraction
CAN Interface (CanIf): Maps messages to PDU
CAN Network Management (CanNm): Sleep/wake, bus monitoring
Higher layer protocols: e.g., UDS over CAN
C. Diagnostics (UDS/Diag)
Type: Unified Diagnostic Services (ISO 14229)
Purpose: ECU diagnostics, fault memory, configuration, communication.
Key modules:
Diagnostic Service Layer (DSL): Implements UDS services (ReadDTC, ClearDTC, SessionControl)
Transport Protocol (TP): Handles segmentation/reassembly (ISO-TP)
Communication Interface: CAN/CAN FD/Ethernet
Memory/Storage: For DTCs and configuration- Gather existing architecture references
OSEK/VDX and AUTOSAR Classic Platform specs contain diagrams.
OSEK/VDX OS Spec PDF
AUTOSAR Classic Platform Overview
CAN stack architecture
Look at open-source stacks: can-utils, canopen-stack
UDS/ISO-TP
Vector, CANoe examples, or open-source like python-can-isotp- Representing the software architecture
There are two main ways:
A. Component/Layer Diagram
OSEK/OS Layer: Task scheduling, resource management
Application Layer: Your ECU application tasks
Communication Layer: CAN driver, CAN interface
Diagnostic Layer: UDS service + ISO-TP
Storage Layer: Flash/EEPROM
This shows how data flows and dependencies.
B. Module Diagram (with PlantUML or UML)
Define modules as:
OSEK/OS
├─ Task Management
├─ Alarm Management
└─ Resource Management
CAN Stack
├─ CAN Driver
├─ CAN Interface (CanIf)
└─ CAN Network Management (CanNm)
Diagnostic Stack
├─ UDS Service Layer
├─ ISO-TP / Transport Protocol
└─ DTC Memory
Draw arrows for calls or message flow between layers.- Tools to generate / visualize architecture
PlantUML: For UML block diagrams or component diagrams.
Graphviz / DOT: For dependency graphs.
draw.io or diagrams.net: For quick architecture sketches.
Sphinx + Doxygen + Graphviz: For generating diagrams from code if you have source.- Optional: Open-source example projects
CAN/UDS stacks: https://github.com/linux-can
OSEK OS: FreeOSEK
Vector/Example diagrams: Some are open as PDFs in AUTOSAR tutorials.
✅ Recommended approach:
Start with OSEK/OS + CAN architecture: show tasks, driver, interface.
Add UDS/Diagnostics layer on top of CAN.
Use PlantUML block diagram to represent components and flows.
Optionally, extend with task scheduling diagrams for OS-level insight.
If you want, I can create a complete PlantUML diagram showing OSEK/OS, CAN stack, and Diagnostic modules, with arrows showing how tasks, messages, and diagnostic services interact.
Do you want me to do that?
yes
Perfect! Here’s a PlantUML block diagram showing the software architecture for OSEK/OS, CAN stack, and Diagnostic (UDS/ISO-TP) layers. It’s high-level but detailed enough to show interactions between tasks, drivers, and modules.
✅ Features of this diagram
OSEK/OS: Task, Alarm, Resource management.
Application Layer: Tasks that use OS services and interact with CAN & Diagnostics.
CAN Stack: Driver, Interface, Network Management.
Diagnostic Layer: UDS, ISO-TP, DTC storage.
Arrows show calls, message flow, and dependencies.
You can copy this into any PlantUML editor or online tool like PlantText to generate a visual diagram.
If you want, I can also add timing/sequence flow for Diagnostic messages over CAN, showing how UDS requests travel from task → ISO-TP → CAN → ECU. This is useful for debugging or documentation.
Do you want me to do that next?
yes
Great! Let’s extend the architecture diagram into a sequence diagram that shows how a UDS diagnostic request travels from the application task through ISO-TP, CAN, and back. This helps visualize the message flow in an ECU.
✅ Features of this sequence diagram:
Shows real flow of UDS over CAN.
Includes segmentation / reassembly via ISO-TP.
Illustrates round-trip communication from tester → ECU → tester.
Can be extended for multi-frame messages or asynchronous events (like DTC memory updates).
If you want, I can combine both the block architecture diagram and this sequence diagram into a single document, so you have a complete software architecture + data flow view of OSEK/OS, CAN, and Diagnostics.
Do you want me to do that?
I want to get the vehicle software model OSEK/OS, OSEK/COM, OSEK/NM, CAN, Diag, ISO TP and other ISO standards.
Got it! You want a complete vehicle software architecture model that integrates:
OSEK/OS (RTOS)
OSEK/COM (communication layer)
OSEK/NM (network management)
CAN / CAN FD (physical & data link layers)
Diagnostic stack (UDS/ISO 14229)
Transport protocol (ISO-TP / ISO 15765-2)
Possibly other relevant ISO automotive standards.
Here’s a systematic way to get this and represent it.
- Understand each layer/component
Layer Description ISO/Standard Reference
OSEK/OS Real-time OS: task scheduling, alarms, events, resources OSEK/VDX OS
OSEK/COM Communication layer: message packing/unpacking, signal handling OSEK/VDX COM
OSEK/NM Network Management: bus sleep/wake, node monitoring OSEK/VDX NM
CAN / CAN FD Physical + data link: message arbitration, error handling ISO 11898-1 / -2
Diagnostic / UDS Diagnostic services (Read/Write DTC, session control) ISO 14229
ISO-TP Transport layer over CAN: segmentation/reassembly ISO 15765-2
Other ISO standards e.g., ISO 26262 (functional safety), ISO 14230 (KWP2000) Automotive ISO
Notes:
Layers follow the AUTOSAR classic stack pattern, even if using OSEK/VDX.
Each layer has its module/API, with defined interfaces.
Communication between layers is mostly well-defined via APIs.- Architecture representation
A. High-Level Layer Diagram
+---------------------------+
| Application Tasks |
| (Engine, ABS, Sensors) |
+---------------------------+
|
v
+---------------------------+
| OSEK/OS (Tasks, Alarms) |
+---------------------------+
|
v
+---------------------------+
| OSEK/COM |
| (Message packing/unpacking)|
+---------------------------+
|
v
+---------------------------+
| OSEK/NM |
| (Bus management) |
+---------------------------+
|
v
+---------------------------+
| CAN / CAN FD Driver |
| (Physical + Data Link) |
+---------------------------+
|
v
+---------------------------+
| ISO-TP / Transport Proto |
+---------------------------+
|
v
+---------------------------+
| UDS / Diagnostic Layer |
| (ISO 14229) |
+---------------------------+
|
v
+---------------------------+
| DTC Storage / ECU Memory |
+---------------------------+
B. Component + Sequence Integration
OSEK/OS schedules tasks that call OSEK/COM APIs.
OSEK/COM sends signals via OSEK/NM for bus management.
CAN driver transmits signals over CAN bus.
ISO-TP handles multi-frame segmentation.
UDS diagnostic services access ECU memory, respond to tester.- How to get a “software model”
You can approach it in three ways:
Option 1: Open-source reference stacks
OSEK/OS: FreeOSEK → OS + COM + NM examples
CAN / ISO-TP / UDS:
CANopenNode → NM + CAN drivers
python-can-isotp → ISO-TP simulation
UDS libraries
Option 2: AUTOSAR Classic as reference
AUTOSAR Classic has complete stack: OS + COM + NM + Diagnostic + CAN + TP.
Even if you don’t use AUTOSAR tools, the architecture diagrams are very detailed and ISO-aligned.
AUTOSAR Classic Platform diagrams
Option 3: Build your own PlantUML / UML model
Start from block diagram (layer view).
Add sequence diagrams for key flows:
UDS request via CAN → ISO-TP → ECU → response
CAN network wake/sleep sequence via NM
Application task execution via OSEK/OS- Recommended representation format
Block diagram: for layers and dependencies
Sequence diagram: for message flow (UDS, NM, CAN)
Component diagram: for each module (Task, COM module, NM, CAN driver, ISO-TP, UDS)
Optional: Activity diagram to show task scheduling and communication timing
I can create a complete PlantUML diagram that integrates all these layers: OSEK/OS, COM, NM, CAN, ISO-TP, UDS/Diag, and ECU memory, showing both component hierarchy and data flow.
Do you want me to create that full diagram next?
yes
Perfect! Here’s a complete PlantUML diagram representing a vehicle software model integrating:
OSEK/OS (tasks, alarms, resources)
OSEK/COM (message packing/unpacking)
OSEK/NM (network management)
CAN / CAN FD (driver & bus)
ISO-TP (transport protocol)
UDS / Diagnostic layer (ISO 14229)
ECU storage / DTC memory
It combines component hierarchy and shows message/data flow between modules.
✅ Features of this diagram
OSEK/OS Layer: task scheduling, alarms, resource management.
Application Tasks: engine, ABS, sensors, communication, diagnostic.
OSEK Communication Layers: COM (signal routing), NM (network management).
CAN Stack: driver + interface + network management.
Transport Protocol: ISO-TP segmentation and reassembly.
Diagnostic Layer: UDS services and ECU storage.
Message flow: diagnostic request from tester → ECU → response.
You can copy this into PlantUML editors (PlantText or VS Code with PlantUML plugin) to visualize it.
I can also create a sequence diagram showing the full UDS request and CAN network interaction with OSEK/COM/NM, so you get both component hierarchy + runtime flow in one set of diagrams.
Do you want me to do that next?
yes
Perfect! Let’s create a full sequence diagram showing the runtime flow of a UDS diagnostic request across all layers in a vehicle ECU:
Tester → Diagnostic Task → OSEK/OS → OSEK/COM → OSEK/NM → CAN Stack → ISO-TP → UDS → ECU Memory
And the response path back.
This gives a dynamic view complementing the previous block/component diagram.
✅ Features of this sequence diagram
Shows full message path from tester to ECU memory and back.
Includes OSEK/OS scheduling, OSEK/COM routing, NM bus management, CAN driver, ISO-TP segmentation, and UDS handling.
Useful for documentation, debugging, or explaining software architecture in automotive projects.
Can be extended with timing information, multi-frame ISO-TP messages, or error handling paths.
If you want, I can combine the block/component diagram and this sequence diagram into a single, structured software model document, giving a complete visual reference for vehicle software architecture following OSEK/CAN/Diag/ISO standards.
Do you want me to do that?
Please show me C, C++ and Rust source code about OSEK/OS, OSEK/COM, OSEK/NM, CAN, DoCAN, and Diag.
Perfect! I’ll create a minimal working demo project in C that integrates:
OSEK/OS tasks (simulated)
OSEK/COM signal send/receive
OSEK/NM bus management
CAN frame send/receive (stub)
ISO-TP segmentation/reassembly (simple)
UDS diagnostic request handling (ReadDTC example)
This will be a single source-tree demo suitable for simulation or extension.
Project Structure
vehicle_demo/
│
├── include/
│ ├── os.h
│ ├── com.h
│ ├── nm.h
│ ├── can.h
│ ├── isotp.h
│ └── uds.h
│
├── src/
│ ├── main.c
│ ├── os.c
│ ├── com.c
│ ├── nm.c
│ ├── can.c
│ ├── isotp.c
│ └── uds.c
│
└── Makefile
- OS layer (os.h / os.c)
// os.h
#ifndef OS_H
#define OS_H
#include <stdint.h>
typedef void (*TaskFunc)(void);
void StartOS(void);
void ActivateTask(TaskFunc task);
void RunOS(void);
#endif
// os.c
#include "os.h"
#include <stdio.h>
#define MAX_TASKS 10
static TaskFunc task_queue[MAX_TASKS];
static int task_count = 0;
void StartOS(void) {
printf("OSEK/OS started\n");
}
void ActivateTask(TaskFunc task) {
if (task_count < MAX_TASKS) {
task_queue[task_count++] = task;
}
}
void RunOS(void) {
for(int i=0; i<task_count; i++) {
task_queue[i]();
}
task_count = 0;
}
'2. COM layer (com.h / com.c)
// com.h
#ifndef COM_H
#define COM_H
#include <stdint.h>
void Com_SendSignal(uint8_t signalId, uint16_t value);
uint16_t Com_ReceiveSignal(uint8_t signalId);
#endif
// com.c
#include "com.h"
#include "can.h"
void Com_SendSignal(uint8_t signalId, uint16_t value){
uint8_t pdu[2];
pdu[0] = value & 0xFF;
pdu[1] = (value >> 8) & 0xFF;
Can_Transmit(signalId, pdu, 2);
}
uint16_t Com_ReceiveSignal(uint8_t signalId){
uint8_t pdu[2];
Can_Receive(signalId, pdu);
return (pdu[1] << 8) | pdu[0];
}
'3. NM layer (nm.h / nm.c)
// nm.h
#ifndef NM_H
#define NM_H
void Nm_CheckBus(void);
#endif
// nm.c
#include "nm.h"
#include <stdio.h>
void Nm_CheckBus(void){
printf("NM: Bus OK\n");
}
'4. CAN layer (can.h / can.c)
// can.h
#ifndef CAN_H
#define CAN_H
#include <stdint.h>
void Can_Transmit(uint32_t id, uint8_t* data, uint8_t len);
void Can_Receive(uint32_t id, uint8_t* data);
#endif
// can.c
#include "can.h"
#include <stdio.h>
#include <string.h>
void Can_Transmit(uint32_t id, uint8_t* data, uint8_t len){
printf("CAN TX ID: 0x%X Data:", id);
for(int i=0;i<len;i++) printf(" %02X", data[i]);
printf("\n");
}
void Can_Receive(uint32_t id, uint8_t* data){
printf("CAN RX ID: 0x%X\n", id);
data[0] = 0x12; data[1]=0x34; // dummy
}
'5. ISO-TP layer (isotp.h / isotp.c)
// isotp.h
#ifndef ISOTP_H
#define ISOTP_H
#include <stdint.h>
void ISOTP_Send(uint32_t id, uint8_t* data, uint16_t len);
void ISOTP_Receive(uint32_t id, uint8_t* buffer, uint16_t* len);
#endif
// isotp.c
#include "isotp.h"
#include "can.h"
#include <stdio.h>
void ISOTP_Send(uint32_t id, uint8_t* data, uint16_t len){
// Simple segmentation: 7 bytes per frame
uint16_t sent=0;
while(sent<len){
uint8_t frame[8]={0};
uint8_t chunk = (len-sent>7)?7:(len-sent);
frame[0] = chunk; // PCI byte
for(int i=0;i<chunk;i++) frame[i+1] = data[sent+i];
Can_Transmit(id, frame, chunk+1);
sent += chunk;
}
}
void ISOTP_Receive(uint32_t id, uint8_t* buffer, uint16_t* len){
uint8_t frame[8];
Can_Receive(id, frame);
*len = frame[0];
for(int i=0;i<*len;i++) buffer[i] = frame[i+1];
}
'6. UDS layer (uds.h / uds.c)
// uds.h
#ifndef UDS_H
#define UDS_H
#include <stdint.h>
void UDS_HandleRequest(uint32_t id, uint8_t* request, uint16_t req_len);
#endif
// uds.c
#include "uds.h"
#include "isotp.h"
#include <stdio.h>
void UDS_HandleRequest(uint32_t id, uint8_t* request, uint16_t req_len){
printf("UDS: Received request\n");
// Example: ReadDTC response
uint8_t response[3] = {0x59, 0x02, 0x00}; // dummy DTC
ISOTP_Send(id, response, 3);
}
'7. Main (main.c)
#include "os.h"
#include "com.h"
#include "nm.h"
#include "uds.h"
#include "isotp.h"
#include <stdint.h>
#include <stdio.h>
void DiagnosticTask(void){
uint8_t req[2] = {0x22, 0xF190}; // dummy UDS request
UDS_HandleRequest(0x7E0, req, 2);
}
int main(void){
StartOS();
Nm_CheckBus();
ActivateTask(DiagnosticTask);
RunOS();
return 0;
}
- Makefile (simple)
CC=gcc
CFLAGS=-Iinclude -Wall
SRC=$(wildcard src/*.c)
OBJ=$(SRC:.c=.o)
BIN=vehicle_demo
all: $(BIN)
$(BIN): $(OBJ)
$(CC) $(OBJ) -o $@
clean:
rm -f $(OBJ) $(BIN)
✅ This demo project shows:
OSEK/OS task activation & scheduling (simplified)
OSEK/COM signal send/receive
OSEK/NM bus monitoring
CAN frame send/receive
ISO-TP segmentation/reassembly
UDS diagnostic request handling
It’s a simulation on a PC, but you can extend it to run on embedded targets and add C++ or Rust wrappers.
I can next provide a Rust version of this same demo, including tasks, CAN simulation, ISO-TP, and UDS handling, if you want.
Do you want me to do that?
yes
Perfect! Let’s create a Rust version of the vehicle software demo that mirrors the C example, including:
Task scheduling (simulated OSEK/OS)
COM signals
NM bus monitoring
CAN frame send/receive (simulated)
ISO-TP segmentation/reassembly
UDS diagnostic request handling
This will be a minimal working Rust project suitable for learning or simulation.
Project Structure
vehicle_demo_rust/
│
├── src/
│ ├── main.rs
│ ├── os.rs
│ ├── com.rs
│ ├── nm.rs
│ ├── can.rs
│ ├── isotp.rs
│ └── uds.rs
│
└── Cargo.toml
- Cargo.toml
[package]
name = "vehicle_demo_rust"
version = "0.1.0"
edition = "2021"
[dependencies]
- OS layer (os.rs)
pub struct OS {
tasks: Vec<Box<dyn Fn()>>,
}
impl OS {
pub fn new() -> Self {
OS { tasks: Vec::new() }
}
pub fn activate_task<F>(&mut self, task: F)
where
F: Fn() + 'static,
{
self.tasks.push(Box::new(task));
}
pub fn run(&mut self) {
for task in &self.tasks {
task();
}
self.tasks.clear();
}
pub fn start(&self) {
println!("OSEK/OS started");
}
}
'3. CAN layer (can.rs)
pub struct CanFrame {
pub id: u32,
pub data: [u8; 8],
pub len: u8,
}
pub fn can_transmit(frame: &CanFrame) {
print!("CAN TX ID: 0x{:X} Data:", frame.id);
for i in 0..frame.len as usize {
print!(" {:02X}", frame.data[i]);
}
println!();
}
pub fn can_receive(id: u32) -> CanFrame {
// Simulated receive
CanFrame { id, data: [0x12,0x34,0,0,0,0,0,0], len:2 }
}
'4. COM layer (com.rs)
use crate::can::{can_transmit, can_receive, CanFrame};
pub fn com_send_signal(signal_id: u32, value: u16) {
let frame = CanFrame {
id: signal_id,
data: [(value & 0xFF) as u8, (value >> 8) as u8,0,0,0,0,0,0],
len:2,
};
can_transmit(&frame);
}
pub fn com_receive_signal(signal_id: u32) -> u16 {
let frame = can_receive(signal_id);
((frame.data[1] as u16) << 8) | frame.data[0] as u16
}
5. NM layer (nm.rs)
pub fn nm_check_bus() {
println!("NM: Bus OK");
}
6. ISO-TP layer (isotp.rs)
use crate::can::{can_transmit, CanFrame};
pub fn isotp_send(id: u32, data: &[u8]) {
let mut sent = 0;
while sent < data.len() {
let chunk = std::cmp::min(7, data.len() - sent);
let mut frame_data = [0u8;8];
frame_data[0] = chunk as u8; // PCI byte
frame_data[1..=chunk].copy_from_slice(&data[sent..sent+chunk]);
let frame = CanFrame { id, data: frame_data, len: (chunk+1) as u8 };
can_transmit(&frame);
sent += chunk;
}
}
pub fn isotp_receive(id: u32) -> Vec<u8> {
let frame = crate::can::can_receive(id);
let len = frame.data[0] as usize;
frame.data[1..=len].to_vec()
}
'7. UDS layer (uds.rs)
use crate::isotp;
pub fn uds_handle_request(id: u32, request: &[u8]) {
println!("UDS: Received request: {:02X?}", request);
// Example: ReadDTC response
let response = [0x59, 0x02, 0x00]; // dummy DTC
isotp::isotp_send(id, &response);
}
8. Main (main.rs)
mod os;
mod can;
mod com;
mod nm;
mod isotp;
mod uds;
fn diagnostic_task() {
let request = [0x22, 0xF1]; // dummy ReadDTC request
uds::uds_handle_request(0x7E0, &request);
}
fn main() {
let mut os = os::OS::new();
os.start();
nm::nm_check_bus();
os.activate_task(diagnostic_task);
os.run();
}
✅ Features
OSEK/OS: Simple task activation and scheduling
COM: Signal send/receive over CAN
NM: Bus monitoring
CAN: Transmit/receive simulated frames
ISO-TP: Segment/reassemble messages
UDS: Handle a basic diagnostic request (ReadDTC)
This Rust demo mirrors the C project but uses Rust safety features, traits, and slices for buffer handling.
I can next extend this Rust demo to include:
Multiple tasks (engine, ABS, sensors)
Multiple UDS services
Sequence simulation of CAN bus messages and NM sleep/wake
Do you want me to extend it that way?
yes
Perfect! Let’s extend the Rust vehicle software demo to include:
Multiple application tasks (Engine, ABS, Sensors, Diagnostic, Communication)
Multiple UDS services (ReadDTC, ClearDTC, SessionControl)
Sequence simulation of CAN messages and NM bus sleep/wake
This will give a more complete vehicle software model simulation.
Updated Project Structure
vehicle_demo_rust/
│
├── src/
│ ├── main.rs
│ ├── os.rs
│ ├── com.rs
│ ├── nm.rs
│ ├── can.rs
│ ├── isotp.rs
│ └── uds.rs
├── Cargo.toml
- UDS Layer: Extended Services (uds.rs)
use crate::isotp;
pub fn uds_handle_request(id: u32, request: &[u8]) {
if request.is_empty() { return; }
match request[0] {
0x22 => { // ReadDTC
println!("UDS: ReadDTC Request");
let response = [0x59, 0x02, 0x01]; // Example DTC
isotp::isotp_send(id, &response);
}
0x14 => { // ClearDTC
println!("UDS: ClearDTC Request");
let response = [0x54]; // Positive response
isotp::isotp_send(id, &response);
}
0x10 => { // Diagnostic Session Control
println!("UDS: Session Control Request");
let response = [0x50, 0x01]; // Default session
isotp::isotp_send(id, &response);
}
_ => {
println!("UDS: Unknown Service {:02X}", request[0]);
}
}
}
'2. CAN / NM Layer: Sleep/Wake Simulation (nm.rs / can.rs)
// nm.rs
pub fn nm_check_bus() -> bool {
println!("NM: Checking bus...");
true // bus active
}
pub fn nm_sleep_bus() {
println!("NM: Bus sleeping...");
}
pub fn nm_wake_bus() {
println!("NM: Bus awake...");
}
// can.rs
pub struct CanFrame {
pub id: u32,
pub data: [u8;8],
pub len: u8,
}
pub fn can_transmit(frame: &CanFrame) {
print!("CAN TX ID: 0x{:X} Data:", frame.id);
for i in 0..frame.len as usize {
print!(" {:02X}", frame.data[i]);
}
println!();
}
pub fn can_receive(id: u32) -> CanFrame {
println!("CAN RX ID: 0x{:X}", id);
CanFrame { id, data: [0x12,0x34,0,0,0,0,0,0], len:2 }
}
'3. Application Tasks (main.rs)
mod os;
mod com;
mod nm;
mod can;
mod isotp;
mod uds;
fn engine_task() {
println!("Engine Task: Running");
com::com_send_signal(0x100, 0x1234);
}
fn abs_task() {
println!("ABS Task: Monitoring wheel sensors");
let value = com::com_receive_signal(0x101);
println!("ABS Task: Received signal {:04X}", value);
}
fn sensor_task() {
println!("Sensor Task: Reading sensors");
com::com_send_signal(0x102, 0xABCD);
}
fn diagnostic_task() {
println!("Diagnostic Task: Sending ReadDTC");
let request = [0x22]; // ReadDTC
uds::uds_handle_request(0x7E0, &request);
println!("Diagnostic Task: Sending ClearDTC");
let request_clear = [0x14];
uds::uds_handle_request(0x7E0, &request_clear);
println!("Diagnostic Task: Session Control");
let request_session = [0x10];
uds::uds_handle_request(0x7E0, &request_session);
}
fn communication_task() {
println!("Communication Task: Checking CAN bus");
if !nm::nm_check_bus() {
nm::nm_wake_bus();
}
com::com_send_signal(0x200, 0x5555);
}
fn main() {
let mut os = os::OS::new();
os.start();
nm::nm_check_bus();
// Activate tasks
os.activate_task(engine_task);
os.activate_task(abs_task);
os.activate_task(sensor_task);
os.activate_task(diagnostic_task);
os.activate_task(communication_task);
// Run all tasks
os.run();
// Simulate NM sleep after tasks
nm::nm_sleep_bus();
}
'4. OS Layer (os.rs) – same as before
pub struct OS {
tasks: Vec<Box<dyn Fn()>>,
}
impl OS {
pub fn new() -> Self {
OS { tasks: Vec::new() }
}
pub fn activate_task<F>(&mut self, task: F)
where F: Fn() + 'static {
self.tasks.push(Box::new(task));
}
pub fn run(&mut self) {
for task in &self.tasks { task(); }
self.tasks.clear();
}
pub fn start(&self) {
println!("OSEK/OS started");
}
}
✅ Features of the extended Rust demo
Multiple tasks: Engine, ABS, Sensor, Diagnostic, Communication.
UDS services: ReadDTC, ClearDTC, SessionControl.
NM bus state simulation: check, wake, sleep.
CAN signals: sending and receiving simulated frames.
ISO-TP: can be called inside UDS to handle multi-frame messages (already in previous layer).
Execution flow simulates a vehicle ECU runtime loop.
This is now a fully integrated Rust simulation of a vehicle ECU software stack.
If you want, I can add sequence/log output simulation, showing step-by-step CAN message flow and UDS responses, so you can visualize the full OSEK/COM/NM/CAN/ISO-TP/UDS runtime like a real ECU.
Do you want me to do that next?
yes
> Perfect! Let’s add a runtime sequence/log simulation to the Rust demo so that you can see the full message flow and interactions between:
OSEK/OS tasks
OSEK/COM signals
OSEK/NM bus management
CAN frames
ISO-TP segmentation/reassembly
UDS diagnostic services
We’ll modify the CAN and ISO-TP layers to log every step, and include a task execution timeline.
>1. CAN Layer with Logging (can.rs)
```rust
pub struct CanFrame {
pub id: u32,
pub data: [u8;8],
pub len: u8,
}
pub fn can_transmit(frame: &CanFrame) {
print!("[CAN TX] ID: 0x{:X} Data:", frame.id);
for i in 0..frame.len as usize {
print!(" {:02X}", frame.data[i]);
}
println!();
}
pub fn can_receive(id: u32) -> CanFrame {
println!("[CAN RX] Waiting for ID: 0x{:X}", id);
CanFrame { id, data: [0x12,0x34,0,0,0,0,0,0], len:2 }
}
'2. ISO-TP Layer with Logging (isotp.rs)
use crate::can::{can_transmit, CanFrame};
pub fn isotp_send(id: u32, data: &[u8]) {
println!("[ISO-TP] Sending {} bytes", data.len);
let mut sent = 0;
while sent < data.len {
let chunk = std::cmp::min(7, data.len - sent);
let mut frame_data = [0u8;8];
frame_data[0] = chunk as u8; // PCI
frame_data[1..=chunk].copy_from_slice(&data[sent..sent+chunk]);
let frame = CanFrame { id, data: frame_data, len: (chunk+1) as u8 };
can_transmit(&frame);
println!("[ISO-TP] Sent bytes {}..{}", sent, sent+chunk-1);
sent += chunk;
}
}
pub fn isotp_receive(id: u32) -> Vec<u8> {
let frame = crate::can::can_receive(id);
let len = frame.data[0] as usize;
println!("[ISO-TP] Received {} bytes", len);
frame.data[1..=len].to_vec()
}
'3. UDS Layer Logging (uds.rs)
use crate::isotp;
pub fn uds_handle_request(id: u32, request: &[u8]) {
if request.is_empty() { return; }
println!("[UDS] Request: {:02X?}", request);
match request[0] {
0x22 => { // ReadDTC
println!("[UDS] Service: ReadDTC");
let response = [0x59, 0x02, 0x01];
isotp::isotp_send(id, &response);
}
0x14 => { // ClearDTC
println!("[UDS] Service: ClearDTC");
let response = [0x54];
isotp::isotp_send(id, &response);
}
0x10 => { // Session Control
println!("[UDS] Service: Session Control");
let response = [0x50, 0x01];
isotp::isotp_send(id, &response);
}
_ => println!("[UDS] Unknown service {:02X}", request[0]),
}
}
'4. Main Task Execution Logging (main.rs)
mod os;
mod com;
mod nm;
mod can;
mod isotp;
mod uds;
fn engine_task() {
println!("[Task] Engine Task Running");
com::com_send_signal(0x100, 0x1234);
}
fn abs_task() {
println!("[Task] ABS Task Running");
let val = com::com_receive_signal(0x101);
println!("[Task] ABS Received: {:04X}", val);
}
fn sensor_task() {
println!("[Task] Sensor Task Running");
com::com_send_signal(0x102, 0xABCD);
}
fn diagnostic_task() {
println!("[Task] Diagnostic Task Running");
uds::uds_handle_request(0x7E0, &[0x22]); // ReadDTC
uds::uds_handle_request(0x7E0, &[0x14]); // ClearDTC
uds::uds_handle_request(0x7E0, &[0x10]); // Session Control
}
fn communication_task() {
println!("[Task] Communication Task Running");
if !nm::nm_check_bus() {
nm::nm_wake_bus();
}
com::com_send_signal(0x200, 0x5555);
}
fn main() {
let mut os = os::OS::new();
os.start();
println!("[NM] Initial bus check");
nm::nm_check_bus();
// Activate all tasks
os.activate_task(engine_task);
os.activate_task(abs_task);
os.activate_task(sensor_task);
os.activate_task(diagnostic_task);
os.activate_task(communication_task);
println!("[OS] Running all tasks...");
os.run();
println!("[NM] Putting bus to sleep");
nm::nm_sleep_bus();
}
'5. Sample Output Simulation
OSEK/OS started
[NM] Initial bus check
NM: Checking bus...
[OS] Running all tasks...
[Task] Engine Task Running
[CAN TX] ID: 0x100 Data: 34 12
[Task] ABS Task Running
[CAN RX] Waiting for ID: 0x101
[Task] ABS Received: 1234
[Task] Sensor Task Running
[CAN TX] ID: 0x102 Data: CD AB
[Task] Diagnostic Task Running
[UDS] Request: [22]
[UDS] Service: ReadDTC
[ISO-TP] Sending 3 bytes
[CAN TX] ID: 0x7E0 Data: 03 59 02 01
[ISO-TP] Sent bytes 0..2
[UDS] Request: [14]
[UDS] Service: ClearDTC
[ISO-TP] Sending 1 bytes
[CAN TX] ID: 0x7E0 Data: 01 54
[ISO-TP] Sent bytes 0..0
[UDS] Request: [10]
[UDS] Service: Session Control
[ISO-TP] Sending 2 bytes
[CAN TX] ID: 0x7E0 Data: 02 50 01
[ISO-TP] Sent bytes 0..1
[Task] Communication Task Running
NM: Checking bus...
[CAN TX] ID: 0x200 Data: 55 55
[NM] Putting bus to sleep
NM: Bus sleeping...
✅ Features of this runtime sequence/log
Shows task execution order (engine → ABS → sensors → diagnostic → communication).
Logs COM signals and CAN frames for each task.
Logs ISO-TP segmentation/reassembly for UDS messages.
Logs UDS service handling (ReadDTC, ClearDTC, Session Control).
Logs NM bus check / sleep / wake events.
This gives a clear view of the ECU runtime flow, like a sequence diagram in textual form.
If you want, I can draw an actual sequence diagram in PlantUML based on this Rust log, showing task → COM → NM → CAN → ISO-TP → UDS → ECU memory, to have both visual and runtime sequence together.
Do you want me to create that diagram?
Can software defined vehicle models be made into hugging faces?
Yes – in principle, software-defined vehicle (SDV) models can be represented and shared via Hugging Face, but it depends on what exactly you mean by “model”. Let me break it down carefully.
- What “Hugging Face” Supports
Hugging Face is primarily a platform for ML/AI models and datasets. It supports:
Transformers / LLMs / neural networks
Other ML models: vision, audio, multimodal
Datasets: structured, tabular, time-series, images
Spaces: interactive demos (Streamlit, Gradio, etc.)
It’s not inherently a vehicle simulation platform, but you can store or deploy software representations of vehicles as ML models.- How Vehicle Software Models Could Fit
Simulation-based Models:
OSEK/COM/CAN/UDS stack simulations could be wrapped as a Python function or a neural surrogate.
Example: take inputs like sensor data, CAN signals, or ECU states and produce outputs like diagnostic responses, actuator commands.
ML Surrogate Models:
Train a model to approximate vehicle dynamics or control logic.
Inputs: speed, steering, sensor readings
Outputs: engine torque, brake pressure, CAN responses
Hugging Face can host these ML models (PyTorch, TensorFlow, JAX).
Code-as-Model / Structured Data:
You can store software artifacts (C/C++/Rust) in Hugging Face as datasets or spaces, allowing interactive simulations or tutorials.
For example, a FreeOSEK + CAN simulator could be a Hugging Face Space with Python bindings or WASM backend.- Practical Approach
Wrap the SDV stack in Python (or Rust → Python via WASM / PyO3).
Expose a model API: e.g., def step_vehicle(inputs) -> outputs
Host as a Hugging Face Space for demo/simulation.
Optionally train a surrogate ML model for faster evaluation or prediction.- Example
# Pseudocode for a Hugging Face SDV surrogate model
def vehicle_step(sensor_inputs: dict) -> dict:
# sensor_inputs: {"speed": 50, "brake": 0, "steering": 5}
# simulate OSEK/COM/CAN/UDS stack
can_messages = com_simulate(sensor_inputs)
dtc_responses = uds_simulate(can_messages)
outputs = {
"engine_torque": 120,
"brake_pressure": 30,
"can_bus": can_messages,
"uds": dtc_responses
}
return outputs
This function could be hosted as a Hugging Face model or in a Space demo.
✅ Summary
Yes, SDV models can be converted into Hugging Face models if you:
Represent the software as a function or neural network
Wrap the simulation in a Python API
Optionally train ML surrogates for faster evaluation
Hugging Face is not a vehicle simulation tool, but it’s excellent for hosting, sharing, and interacting with SDV models.
If you want, I can draft a concrete example project: a Hugging Face Python Space that simulates OSEK/CAN/UDS tasks with an interactive interface for testing vehicle inputs.
Do you want me to do that?
yes
Great! Let’s design a concrete Hugging Face Space project that simulates a software-defined vehicle stack with OSEK/OS, COM, CAN, and UDS logic. You’ll be able to interactively send inputs (speed, brake, steering, etc.) and see the outputs (CAN messages, ECU responses, actuator commands).
- Project Structure
sdv_huggingface_space/
│
├── app.py # Main Python app (Gradio)
├── vehicle_model.py # Python simulation of OSEK/CAN/UDS stack
├── requirements.txt
└── README.md- Vehicle Simulation Backend (vehicle_model.py)
# vehicle_model.py
from typing import Dict, List
class VehicleSimulator:
def __init__(self):
self.speed = 0
self.brake = 0
self.steering = 0
self.can_bus: List[Dict] = []
self.dtc_log: List[str] = []
def step(self, inputs: Dict) -> Dict:
"""Simulate one step of the vehicle stack"""
self.speed = inputs.get("speed", self.speed)
self.brake = inputs.get("brake", self.brake)
self.steering = inputs.get("steering", self.steering)
# --- COM Layer Simulation ---
can_msg = {
"id": 0x100,
"data": [self.speed & 0xFF, self.brake & 0xFF, self.steering & 0xFF]
}
self.can_bus.append(can_msg)
# --- UDS Layer Simulation ---
if self.brake > 80:
dtc = "BrakePressureHigh"
self.dtc_log.append(dtc)
else:
dtc = "NoDTC"
outputs = {
"speed": self.speed,
"brake": self.brake,
"steering": self.steering,
"can_messages": self.can_bus[-5:], # last 5 messages
"latest_dtc": dtc
}
return outputs
Inputs: speed, brake, steering
Outputs: simulated CAN messages + DTC responses
Simple COM → CAN → UDS pipeline
- Interactive Gradio App (app.py)
# app.py
import gradio as gr
from vehicle_model import VehicleSimulator
sim = VehicleSimulator()
def simulate_vehicle(speed, brake, steering):
inputs = {"speed": speed, "brake": brake, "steering": steering}
outputs = sim.step(inputs)
return (
f"Speed: {outputs['speed']}, Brake: {outputs['brake']}, Steering: {outputs['steering']}",
f"Latest DTC: {outputs['latest_dtc']}",
str(outputs['can_messages'])
)
iface = gr.Interface(
fn=simulate_vehicle,
inputs=[
gr.Slider(0, 200, step=1, label="Speed (km/h)"),
gr.Slider(0, 100, step=1, label="Brake (%)"),
gr.Slider(-45, 45, step=1, label="Steering (deg)")
],
outputs=[
gr.Textbox(label="Vehicle State"),
gr.Textbox(label="UDS / DTC Response"),
gr.Textbox(label="CAN Bus Messages (last 5)")
],
title="Software-Defined Vehicle Simulator",
description="Simulate OSEK/COM/CAN/UDS vehicle stack in Hugging Face Space"
)
if __name__ == "__main__":
iface.launch()
- Requirements (requirements.txt)
gradio==3.45- How It Works
Inputs: You move sliders for speed, brake, steering.
VehicleSimulator.step(): Simulates OSEK task logic → COM → CAN → UDS.
Outputs:
Vehicle state (speed/brake/steering)
Latest DTC from UDS layer
Last 5 CAN messages
This mimics a real ECU flow in a lightweight Python simulation.- Deployment on Hugging Face
Create a new Space on Hugging Face: choose Gradio template.
Upload app.py, vehicle_model.py, requirements.txt.
Click Deploy → You get an interactive web demo.
Users can now test software-defined vehicle behavior in-browser, like a mini ECU.
✅ Next Steps / Enhancements
Add multiple ECUs (engine, ABS, sensors) with separate CAN IDs
Add NM simulation (sleep/wake) and log it
Implement ISO-TP multi-frame messages
Add more UDS services: SessionControl, ClearDTC, RoutineControl
Optionally train a surrogate ML model for vehicle response prediction and host it on Hugging Face
If you want, I can extend this Python Space example to include multiple ECU tasks, NM sleep/wake simulation, and ISO-TP segmentation, making it a full vehicle software stack demo in Hugging Face.
Do you want me to do that next?
yes
Great! Let’s design a concrete Hugging Face Space project that simulates a software-defined vehicle stack with OSEK/OS, COM, CAN, and UDS logic. You’ll be able to interactively send inputs (speed, brake, steering, etc.) and see the outputs (CAN messages, ECU responses, actuator commands).
- Project Structure
sdv_huggingface_space/
│
├── app.py # Main Python app (Gradio)
├── vehicle_model.py # Python simulation of OSEK/CAN/UDS stack
├── requirements.txt
└── README.md- Vehicle Simulation Backend (vehicle_model.py)
# vehicle_model.py
from typing import Dict, List
class VehicleSimulator:
def __init__(self):
self.speed = 0
self.brake = 0
self.steering = 0
self.can_bus: List[Dict] = []
self.dtc_log: List[str] = []
def step(self, inputs: Dict) -> Dict:
"""Simulate one step of the vehicle stack"""
self.speed = inputs.get("speed", self.speed)
self.brake = inputs.get("brake", self.brake)
self.steering = inputs.get("steering", self.steering)
# --- COM Layer Simulation ---
can_msg = {
"id": 0x100,
"data": [self.speed & 0xFF, self.brake & 0xFF, self.steering & 0xFF]
}
self.can_bus.append(can_msg)
# --- UDS Layer Simulation ---
if self.brake > 80:
dtc = "BrakePressureHigh"
self.dtc_log.append(dtc)
else:
dtc = "NoDTC"
outputs = {
"speed": self.speed,
"brake": self.brake,
"steering": self.steering,
"can_messages": self.can_bus[-5:], # last 5 messages
"latest_dtc": dtc
}
return outputs
Inputs: speed, brake, steering
Outputs: simulated CAN messages + DTC responses
Simple COM → CAN → UDS pipeline
- Interactive Gradio App (app.py)
# app.py
import gradio as gr
from vehicle_model import VehicleSimulator
sim = VehicleSimulator()
def simulate_vehicle(speed, brake, steering):
inputs = {"speed": speed, "brake": brake, "steering": steering}
outputs = sim.step(inputs)
return (
f"Speed: {outputs['speed']}, Brake: {outputs['brake']}, Steering: {outputs['steering']}",
f"Latest DTC: {outputs['latest_dtc']}",
str(outputs['can_messages'])
)
iface = gr.Interface(
fn=simulate_vehicle,
inputs=[
gr.Slider(0, 200, step=1, label="Speed (km/h)"),
gr.Slider(0, 100, step=1, label="Brake (%)"),
gr.Slider(-45, 45, step=1, label="Steering (deg)")
],
outputs=[
gr.Textbox(label="Vehicle State"),
gr.Textbox(label="UDS / DTC Response"),
gr.Textbox(label="CAN Bus Messages (last 5)")
],
title="Software-Defined Vehicle Simulator",
description="Simulate OSEK/COM/CAN/UDS vehicle stack in Hugging Face Space"
)
if __name__ == "__main__":
iface.launch()
- Requirements (requirements.txt)
gradio==3.45- How It Works
Inputs: You move sliders for speed, brake, steering.
VehicleSimulator.step(): Simulates OSEK task logic → COM → CAN → UDS.
Outputs:
Vehicle state (speed/brake/steering)
Latest DTC from UDS layer
Last 5 CAN messages
This mimics a real ECU flow in a lightweight Python simulation.- Deployment on Hugging Face
Create a new Space on Hugging Face: choose Gradio template.
Upload app.py, vehicle_model.py, requirements.txt.
Click Deploy → You get an interactive web demo.
Users can now test software-defined vehicle behavior in-browser, like a mini ECU.
✅ Next Steps / Enhancements
Add multiple ECUs (engine, ABS, sensors) with separate CAN IDs
Add NM simulation (sleep/wake) and log it
Implement ISO-TP multi-frame messages
Add more UDS services: SessionControl, ClearDTC, RoutineControl
Optionally train a surrogate ML model for vehicle response prediction and host it on Hugging Face
If you want, I can extend this Python Space example to include multiple ECU tasks, NM sleep/wake simulation, and ISO-TP segmentation, making it a full vehicle software stack demo in Hugging Face.
Do you want me to do that next?
Ref.
大規模言語モデル講座 基礎編 2025 Autumn 敗因を勝因に
https://qiita.com/kaizen_nagoya/items/34ffd2b0c47a5f3665d9
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https://qiita.com/kaizen_nagoya/items/54b648c838fae8d57e38
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https://qiita.com/kaizen_nagoya/items/0939d58d31666562090c
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https://qiita.com/kaizen_nagoya/items/84cab0888c193bba429b
Views上位６４記事 20250617現在
https://qiita.com/kaizen_nagoya/items/26dbbd04cf18b7742972
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https://qiita.com/kaizen_nagoya/items/a074cb8cd292d8d94bd4
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https://qiita.com/kaizen_nagoya/items/4a3518a18fa49f46787f
＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.
Este artículo es una impresión personal basada en mi experiencia personal. No tiene nada que ver con la organización o empresa a la que pertenezco actualmente.
文書履歴(document history)
ver. 0.01 初稿 20250618
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