13 papers
SafeVantage: Vantage-Aware Memory for Reliable Embodied Decisions
Sean Hardesty Lewis, Zuyi Guo, Benwang Chen +4
Reliable embodied decisions under partial observability require informative observations and sufficient supporting evidence. However, semantic scores alone do not reveal which view…
VeriFuse: Bounded Vision-Language Arbitration and Reason-Guided Refinement for Cooperative 3D Perception
Hongyi Lin, Yiyao Liu, Qi Kang +4
Vision-language models (VLMs) have demonstrated strong scene understanding and semantic judgment across diverse tasks, but their appropriate role in cooperative perception remains…
SSP-DMGTimeNet: Physics-Constrained Learning for Spatiotemporal Trajectory Prediction of Vehicle Platoons
Yuhang Wang, Kailang Ma, Zirui Li +4
Existing car-following prediction methods mainly optimize trajectory accuracy, while rarely considering whether predicted disturbances propagate realistically along a vehicle plato…
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…
RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios
Qichao Liu, Heye Huang, Shiyue Zhao +3
Ensuring the safety of autonomous vehicles (AVs) in long-tail scenarios remains a critical challenge, particularly under high uncertainty and complex multi-agent interactions. To a…
Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective
Heye Huang, Zheng Li, Hao Cheng +4
Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. Thi…
Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning
Peng Zhang, Heye Huang, Hang Zhou +3
This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability…
Online Physical Enhanced Residual Learning for Connected Autonomous Vehicles Platoon Centralized Control
Hang Zhou, Heye Huang, Peng Zhang +3
This paper introduces an online physical enhanced residual learning (PERL) framework for Connected Autonomous Vehicles (CAVs) platoon, aimed at addressing the challenges posed by t…