Teach Your Self Autonomous Driving System

Introduction

Hi! This is my personal page to record some learning methods and materials about the Autonomous Driving System(ADS) or Automated Vehicles(AV). This page may contain some learning material about specific majors (mainly about CS or AI), some SOTA papers, and also some research tools tutorial like tutorial for VTD, Apollo, etc. Because of my researching focus, this page may focuses on the field of ADS safety, testing, and evaluation.

Key words: Scenario-based testing, SOTIF, Testing acceleration

Table of Contents

1. Research topics
   • [Specific Majors]
   • State-of-the-Art (SOTA) Papers
2. Research Tools Tutorials
   • VTD Tutorial
   • Apollo Tutorial
3. Focus Areas
   • ADS Safety
   • Testing
   • Evaluation

News

Date	Changes Made
2024-01-16	Initial release
2024-
2024-

To-do list

Task	Status	Date
Define the ToC and the detailed contents	- [ ]	2024-01-16
Update paper list	- [ ]	2024-01-16
Task 3	- [ ]	2024-01-17
Task 4	- [ ]	2024-01-17

SOTA Papers

1. Nalic, D. et al. Stress Testing Method for Scenario-Based Testing of Automated Driving Systems. IEEE Access 8, 224974–224984 (2020).
2. Peng, L. et al. SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving. Preprint at https://doi.org/10.48550/arXiv.2211.04009 (2022).
3. Putze, L., Westhofen, L., Koopmann, T., Böde, E. & Neurohr, C. On Quantification for SOTIF Validation of Automated Driving Systems. Preprint at https://doi.org/10.48550/arXiv.2304.10170 (2023).
4. Reichenbächer, C. et al. Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems. in Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems 134–142 (2022). doi:10.5220/0011081500003191.
5. Ponn, T., Breitfuß, M., Yu, X. & Diermeyer, F. Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data. in 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER) 1–10 (2020). doi:10.1109/EVER48776.2020.9242539.
6. Zhang, X. et al. Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study. IEEE Transactions on Software Engineering 49, 991–1026 (2023).
7. Feng, S. et al. Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620–627 (2023).
8. Westhofen, L. et al. Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art. Arch Computat Methods Eng 30, 1–35 (2023).
9. Karunakaran, D., Berrio, J. S., Worrall, S. & Nebot, E. Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data. (2022) doi:10.48550/ARXIV.2203.07521.
10. Xing, X., Jia, T., Chen, J., Xiong, L. & Yu, Z. An Ontology-based Method to Identify Triggering Conditions for Perception Insufficiency of Autonomous Vehicles. Preprint at https://doi.org/10.48550/arXiv.2210.08724 (2022).
11. Liu, W. et al. A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles. IEEE Internet of Things Journal 1–1 (2023) doi:10.1109/JIOT.2023.3307002.
12. Ding, W. et al. A Survey on Safety-Critical Driving Scenario Generation—A Methodological Perspective. IEEE Transactions on Intelligent Transportation Systems 24, 6971–6988 (2023).

Research Tools Tutorials

VTD Tutorial

Apollo Tutorial

Focus Areas

ADS Safety

Testing

Evaluation