ShisatoYano/AutonomousVehicleControlBeginnersGuide resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1647 stars · 240 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Python sample codes and documents explaining autonomous vehicle control algorithms such as localization, mapping, path planning, path tracking, and perception. It is structured like a technical guide book for beginners studying the algorithms and software architectures behind self-driving systems.

Use cases

• learn autonomous vehicle control algorithms from scratch
• study extended kalman filter localization with python examples
• understand path planning algorithms like A* and RRT
• implement pure pursuit path tracking simulation
• learn SLAM and occupancy grid mapping basics
• study perception and object detection algorithms
• find reference code for autonomous driving software architecture

When to choose

• you are a beginner wanting to learn self-driving algorithms with runnable Python simulations
• you want well-documented sample implementations of localization, mapping, planning, and tracking
• you prefer learning through a guide-book style repository with visual simulation examples

When to avoid

• you need production-ready autonomous driving software for a real vehicle
• you require high-performance or real-time capable implementations
• you need a maintained library with API guarantees rather than educational sample code

Facets

learning-resource · maturity active

simulation machine-learning data-visualization developer-tools autonomous-vehicles robotics education simulation tutorials python cross-platform autonomous-driving slam localization path-planning path-tracking perception mapping kalman-filter sample-code technical-guide algorithms

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ShisatoYano/AutonomousVehicleControlBeginnersGuide")