Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions-Reference-Cited by-同舟云学术

Learning Configurations of Operating Environment of Autonomous Vehicles to Maximize their Collisions

Author:

Lu Chengjie¹^ORCID,Shi Yize¹,Zhang Huihui²,Zhang Man³^ORCID,Wang Tiexin¹^ORCID,Yue Tao⁴^ORCID,Ali Shaukat⁵^ORCID

Affiliation:

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

3. Kristiania University College, Oslo, Norway

4. Nanjing University of Aeronautics and Astronautics, Nanjing, China

5. Simula Research Laboratory, Oslo, Norway

Funder

National Natural Science Foundation of China

Co-evolver Project

Norges Forskningsråd

Natural Science Foundation of Shandong Province

European Research Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Software

Link

Reference83 articles.

3. Finding critical scenarios for automated driving systems: A systematic literature review;zhang,2021

4. On a formal model of safe and scalable self-driving cars;shalev-shwartz,2017

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Evolve the Model Universe of a System Universe;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

2. DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing;2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR);2023-05

3. Evolutionary Computation and Reinforcement Learning for Cyber-physical System Design;2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion);2023-05

4. Uncertainty-aware Robustness Assessment of Industrial Elevator Systems;ACM Transactions on Software Engineering and Methodology;2022-12-12

5. Towards realizing test conditions for automated vehicles;Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings;2022-10-23