A curated collection of the latest research papers on autonomous driving from arXiv This repository is automatically updated daily to bring you the most recent advances in self-driving technology (papers from the last 6 months)
This repository tracks recent research papers in autonomous driving, covering topics including:
- Perception (object detection, segmentation, tracking)
- Planning and decision-making
- Control systems
- Prediction and forecasting
- Simulation environments
- End-to-end learning approaches
- Mapping and localization
- Safety and verification
- Datasets and benchmarks
Papers are automatically fetched from arXiv and categorized by topic for easy navigation.
- 🕐 Daily Updates: Automatically updated every day with the latest papers
- 🎯 Smart Categorization: Papers are organized into 10 main categories
- 🏷️ Recency Badges: Visual indicators show how recent each paper is
- 🔍 Easy Navigation: Table of contents and category-based organization
- 📄 Direct Links: Quick access to arXiv abstracts and PDFs
- 📊 Statistics: Track the number of papers in each category
| Category | Paper Count |
|---|
This repository uses automation to stay up-to-date with the latest research:
- Automated Fetching: Python script queries the arXiv API daily using relevant keywords
- Smart Categorization: Papers are categorized by topic using keyword analysis
- Auto-Generated README: This README is automatically generated with formatted paper information
- GitHub Actions: Updates run automatically every day at 00:00 UTC
Want to run the scraper locally or contribute to the project?
# Clone the repository
git clone https://github.com/qinjing/AlphaAD.git
cd AlphaAD
# The script uses only Python standard library (no external dependencies)
python3 scrape_arxiv.pyRequirements: Python 3.11 or higher
Contributions are welcome! Here are some ways you can contribute:
- Improve categorization: Suggest better keywords or categories for paper classification
- Add features: Propose new features like filtering by date range, author search, etc.
- Fix bugs: Report or fix any issues you find
- Enhance documentation: Help improve the README or code comments
To contribute:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
| Category | Description |
|---|---|
| Perception | Object detection, segmentation, tracking, sensor fusion |
| Planning | Path planning, motion planning, trajectory optimization |
| Control | Vehicle control, MPC, steering, acceleration |
| Prediction | Trajectory prediction, intent prediction, forecasting |
| Simulation | Simulation environments, synthetic data |
| End-to-End Learning | Imitation learning, reinforcement learning |
| Mapping & Localization | SLAM, HD maps, visual odometry |
| Safety & Verification | Safety verification, robust testing |
| Dataset & Benchmark | Dataset collections, benchmarks |
| General | Other autonomous driving research |
This project is licensed under the MIT License - see the LICENSE file for details.
- Issues: Open an issue for bugs or feature requests
- Discussions: Start a discussion for questions or ideas
⭐ If you find this repository helpful, consider giving it a star!
Made with ❤️ by the autonomous driving community
Note: This is an automated repository. Papers are fetched from arXiv and categorized algorithmically. Categorization may not always be perfect. Please report any misclassified papers.