AutonomousVehicleControlBeginnersGuide

Python sample codes and documents about Autonomous vehicle control algorithm. This project can be used as a technical guide book to study the algorithms and the software architectures for beginners.

Table of Contents

• What is this?
• Goal of this project
• Requirements
• How to use
• Examples of Simulation
  • Localization
    • Extended Kalman Filter Localization
    • Unscented Kalman Filter Localization
    • Particle Filter Localization
  • Mapping
    • Binary Occupancy Grid Map
    • Cost Map
    • Potential Field Map
    • NDT Map
  • Path Planning
    • A*
    • Bidirectional A*
    • Hybrid A*
    • D*
    • Dijkstra
    • ACO
    • Q-Learning
    • PSO
    • PRM
    • RRT
    • Bidirectional RRT*
    • RRT*
    • Informed RRT*
  • Path Tracking
    • Pure pursuit Path Tracking
    • Adaptive Pure pursuit Path Tracking
    • Rear wheel feedback Path Tracking
    • LQR(Linear Quadratic Regulator) Path Tracking
    • Stanley steering control Path tracking
    • MPPI Path Tracking
  • Perception
    • Rectangle fitting Detection
    • Sensor's Extrinsic Parameters Estimation
• Documents
• License
• Use Case
• Contribution
• Author

What is this?

This is a sample codes collections about Autonomous vehicle control algorithm. Each source codes are implemented with Python to help your understanding. You can fork this repository and use for studying, education or work freely.

Goal of this project

I want to release my own technical book about Autonomous Vehicle algorithms in the future. The book will include all of codes and documents in this repository as contents.

Requirements

Please satisfy with the following requirements on native or VM Linux in advance.
For running each sample codes:

• Python 3.13.x
• Matplotlib
• NumPy
• SciPy

For development:

• pytest (for unit tests)
• pytest-cov (for coverage measurement)

For setting up the environment with Docker:

• VS Code
• Docker

How to use

1. Clone this repository

   $ git clone https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide
   

2. Set up the environment for running each codes

   • Set up with Docker on WSL:
     • Before cloning thi repo, install Docker in advance
     • Clone this repo following the above Step 1
     • Open this repo's folder by VSCode
     • Create Dev Container
     • And then, all required libraries are installed automatically

3. Execute unit tests to confirm the environment were installed successfully

   $ . run_test_suites.sh
   

4. Execute a python script at src/simulations directory

   • For example, when you want to execute localization simulation of Extended Kalman Filter:

     $ python src/simulations/localization/extended_kalman_filter_localization/extended_kalman_filter_localization.py
     

5. Add star to this repository if you like it!!

Examples of Simulation

Localization

Extended Kalman Filter Localization

Unscented Kalman Filter Localization

Particle Filter Localization

Mapping

Binary Occupancy Grid Map

Cost Map

Potential Field Map

NDT Map

Path Planning

A*

Planning

Bidirectional A*

Planning

Hybrid A*

Planning

D*

Planning with dynamic obstacle replanning

Dijkstra

Planning(Reduce frames by sampling every nth node to prevent memory exhaustion)

ACO

Ant Colony Optimization
Author: Banaan Kiamanesh

Q-Learning

Reinforcement learning with a Q-table policy

PSO

Particle Swarm Optimization

PRM

Planning

Elastic Bands

A* seed path smoothed with Elastic Bands optimisation

RRT

Planning

Bidirectional RRT*

Planning

RRT*

Planning

Informed RRT*

Planning

Path Tracking

Pure pursuit Path Tracking

Adaptive Pure pursuit Path Tracking

Rear wheel feedback Path Tracking

LQR(Linear Quadratic Regulator) Path Tracking

Stanley steering control Path Tracking

MPPI Path Tracking

MPC Path Tracking

Perception

Rectangle fitting Detection

Sensor's Extrinsic Parameters Estimation

Estimation by Unscented Kalman Filter

Documents

Design documents of each Python programs are prepared here. The documents are still not completed. They have been being updated. If you found any problems in them, please tell me by creating an issue.
Documents link

License

MIT

Use Case

I started this project to study an algorithm and software development for Autonomous Vehicle system by myself. You can also use this repo for your own studying, education, researching and development.

If this project helps your task, please let me know by creating a issue.
Any paper, animation, video as your output, always welcome!! It will encourage me to continue this project.

Your comment and output is added to this list of user comments.

Contribution

Any contribution by creating an issue or sending a pull request is welcome!! Please check this document about how to contribute.

Author

Shisato Yano