🚦🚗 ADS-Learning 📑📚

NOTE: This repository is been moved back here as a subtask track for my ai-learning repo.

This repository is dedicated to sharing comprehensive literature on the full-stack techniques for autonomous driving systems(ADS), including modeling, testing and some awesome (open-source) utilities.

More to Learn

• This repo is a subtask track for my ai-learning repo, where you can learn more technologies and applicated tasks about the whole AI full-stack

• We built a comprehensive pipeline platform for high-fidelity simulated ADS testing, called ADEPT, where you can equip your own model on the simulated vehicle and run it in the simulated environment to check the effects more vividly and authentically than the cold metric numbers, and even more excitingly, build your own whole ADS to face the unpredictable simulated world with more safety-critical scenarios than the real one

Table of Contents

• ADS Modeling
  • Localization & Mapping
  • Perception & Prediction
  • Planning & Control
  • End-to-End
  • Miscellaneous
• ADS Testing
  • Adversarial Attacks
  • Scenario Generation
  • Oracle
  • Miscellaneous
• ADS Utilities
  • Model
  • Dataset
  • Domain Specific Language
  • Platform
  • Miscellaneous

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Note:

• In each markdown file, the collected paper may be roughly sorted by the published year in descending order, i.e. the newer the paper, the topper it will be put on the file, but it's not one-hundred percent sure since the published year is not always clear.
• The taxonomy is too complicated to be orthogonal, so don't be confused when the same paper is collected in different tracks for many times.