9 papers
Risk-Controllable Multi-View Diffusion for Driving Scenario Generation
Hongyi Lin, Wenxiu Shi, Heye Huang +5
Generating safety-critical driving scenarios is crucial for evaluating and improving autonomous driving systems, but long-tail risky situations are rarely observed in real-world da…
RESPOND: Risk-Enhanced Structured Pattern for LLM-driven Online Node-level Decision-making
Dan Chen, Heye Huang, Tiantian Chen +4
Current LLM-based driving agents that rely on unstructured plain-text memory suffer from low-precision scene retrieval and inefficient reflection. To address this limitation, we pr…
Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge
Yuhang Wang, Heye Huang, Zhenhua Xu +3
Autonomous driving faces critical challenges in rare long-tail events and complex multi-agent interactions, which are scarce in real-world data yet essential for robust safety vali…
Unveiling Uniform Shifted Power Law in Stochastic Human and Autonomous Driving Behavior
Wang Chen, Heye Huang, Ke Ma +4
Accurately simulating rare but safety-critical driving behaviors is essential for the evaluation and certification of autonomous vehicles (AVs). However, current models often fail…
SMART: Scalable Multi-Agent Reasoning and Trajectory Planning in Dense Environments
Heye Huang, Yibin Yang, Wang Chen +3
Multi-vehicle trajectory planning is a non-convex problem that becomes increasingly difficult in dense environments due to the rapid growth of collision constraints. Efficient expl…
REACT: Runtime-Enabled Active Collision-avoidance Technique for Autonomous Driving
Heye Huang, Hao Cheng, Zhiyuan Zhou +3
Achieving rapid and effective active collision avoidance in dynamic interactive traffic remains a core challenge for autonomous driving. This paper proposes REACT (Runtime-Enabled…