# ShisatoYano/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.

Repository: https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide
Canonical: https://ross.abutalabs.com/products/autonomousvehiclecontrolbeginnersguide
Homepage: https://www.eureka-moments-blog.com/
Language: Python
License: MIT
License Family: permissive
Topics: algorithm, autonomous-driving, autonomous-navigation, autonomous-vehicles, calibration, localization, mapping, object-detection, object-tracking, path-planning, path-tracking, perception, python, slam
Last push: 2026-07-26T12:32:30+00:00

## Health v2 (maintenance only)
Score: 75/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 35, longevity 100
- inputs: {"age_days": 1524, "days_push": 38, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1647, forks 240 (observed 2026-08-28T04:05:16.363871+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: simulation, machine-learning, data-visualization, developer-tools
- domain: autonomous-vehicles, robotics, education, simulation, tutorials
- platform: python, cross-platform
- tags: autonomous-driving, slam, localization, path-planning, path-tracking, perception, mapping, kalman-filter, sample-code, technical-guide, algorithms

## Member repositories
- ShisatoYano/AutonomousVehicleControlBeginnersGuide (main) score 75

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.363871+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:17.443057+00:00, confidence not recorded.
  - readme: https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide (fetched 2026-08-28T04:05:16.363871+00:00, sha 6d215be69e54)
  - homepage: https://www.eureka-moments-blog.com/ (fetched 2026-08-29T11:18:37.654455+00:00, sha 71cb5bbdd0be)
  - site_page: https://www.eureka-moments-blog.com/about (fetched 2026-08-29T11:18:37.663892+00:00, sha 95947eb8e209)
- Data as of 2026-08-30T08:39:29.467469+00:00.