5 papers
Emergency Lane-Change Simulation: A Behavioral Guidance Approach for Risky Scenario Generation
Chen Xiong, Cheng Wang, Yuhang Liu +2
In contemporary autonomous driving testing, virtual simulation has become an important approach due to its efficiency and cost effectiveness. However, existing methods usually rely…
Fusing Driver Perceived and Physical Risk for Safety Critical Scenario Screening in Autonomous Driving
Chen Xiong, Ziwen Wang, Deqi Wang +4
Autonomous driving testing increasingly relies on mining safety critical scenarios from large scale naturalistic driving data, yet existing screening pipelines still depend on manu…
AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation
Zhulin Jiang, Zetao Li, Cheng Wang +2
Autonomous driving systems require comprehensive evaluation in safety-critical scenarios to ensure safety and robustness. However, such scenarios are rare and difficult to collect…
Integrating LTL Constraints into PPO for Safe Reinforcement Learning
Maifang Zhang, Hang Yu, Qian Zuo +3
This paper proposes Proximal Policy Optimization with Linear Temporal Logic Constraints (PPO-LTL), a framework that integrates safety constraints written in LTL into PPO for safe r…
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
Cheng Wang, Lingxin Kong, Massimiliano Tamborski +1
Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imi…