5 papers
RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification
Jingzheng Li, Yufei Ge, Qianren Mao +5
Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approaches typically predict scene-l…
End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
Jingzheng Li, Yufei Ge, Zhijun Chen +8
Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical intera…
OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
Bing Li, Wuqi Wang, Yanan Zhang +6
LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is ess…
Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving
Jingzheng Li, Tiancheng Wang, Xingyu Peng +4
Autonomous Driving (AD) systems demand the high levels of safety assurance. Despite significant advancements in AD demonstrated on open-source benchmarks like Longest6 and Bench2Dr…
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Jingzheng Li, Xianglong Liu, Shikui Wei +6
Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous d…