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ShisatoYano/AutonomousVehicleControlBeginnersGuide

Python sample code and guide documents covering autonomous vehicle control algorithms, intended as a beginner-friendly study resource.

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What is it?

This repository collects Python sample programs for autonomous vehicle algorithms, along with design documents describing each program. It covers localization, mapping, path planning, path tracking, and perception, with each algorithm available as a runnable simulation that shows an animated demo.

The project is meant as a technical guide for studying both the algorithms and the software architecture behind an autonomous driving stack. Examples include Extended Kalman Filter localization and NDT map construction. Simulations are run from the src/simulations directory after installing Python 3.13 and the listed dependencies such as NumPy, SciPy, Matplotlib, and, for MPC path tracking, CasADi and do-mpc. A Docker dev container setup is also provided.

The author plans a future simulator where users can swap algorithms at each stage of vehicle navigation and compare results.

Key features

  • 01Python sample code for localization, mapping, path planning, path tracking, and perception algorithms.
  • 02Each algorithm has a runnable simulation with an animated demo.
  • 03Design documents describe the structure of each program.
  • 04Unit tests run with pytest to verify the environment.
  • 05Docker dev container setup installs dependencies automatically.

Use cases

  • Learning autonomous vehicle algorithms from runnable Python examples.
  • Using the code as material for courses or teaching autonomous driving concepts.
  • Prototyping or comparing localization and path tracking methods in simulation.

Quick start

  1. 1

    Clone the repository

    git clone https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide
  2. 2

    Set up the environment (Docker dev container on WSL, or install the listed Python requirements)

  3. 3

    Run the unit tests to confirm the environment works

    . run_test_suites.sh
  4. 4

    Run an example simulation, such as Extended Kalman Filter localization

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

Things to know

  • Requires Python 3.13.x and several libraries such as NumPy, SciPy, CasADi and do-mpc.
  • The README recommends native or VM Linux, and the Docker setup is needed on Windows via WSL.
  • The design documents are still incomplete and being updated.

In collections

Learning & education toolsRobotics & embodied AI projects

Topics

#autonomous-driving#robotics#path-planning#slam#localization#python#simulation#education#algorithm#autonomous-navigation

FAQ

What is AutonomousVehicleControlBeginnersGuide?
Python sample code and guide documents covering autonomous vehicle control algorithms, intended as a beginner-friendly study resource. This repository collects Python sample programs for autonomous vehicle algorithms, along with design documents describing each program. It covers localization, mapping, path planning, path tracking, and perception, with each algorithm available as a runnable simulation that shows an animated demo.
Is AutonomousVehicleControlBeginnersGuide free to use? What license does it have?
AutonomousVehicleControlBeginnersGuide is open source under the MIT license. Check the license terms before using it in commercial products.
Is AutonomousVehicleControlBeginnersGuide still maintained?
Actively maintained. The last commit to AutonomousVehicleControlBeginnersGuide was on Nov 5, 2026.
What can I use AutonomousVehicleControlBeginnersGuide for?
Learning autonomous vehicle algorithms from runnable Python examples. Using the code as material for courses or teaching autonomous driving concepts. Prototyping or comparing localization and path tracking methods in simulation.
How do I install AutonomousVehicleControlBeginnersGuide?
Clone the repository: git clone https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide Set up the environment (Docker dev container on WSL, or install the listed Python requirements) Run the unit tests to confirm the environment works: . run_test_suites.sh Run an example simulation, such as Extended Kalman Filter localization: python src/simulations/localization/extended_kalman_filter_localization/extended_kalman_filter_localization.py
Where can I get AutonomousVehicleControlBeginnersGuide?
The source code and installation instructions are on GitHub: https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide

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