5 papers
Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles
Yicheng Guo, Jiaqi Liu, Chengkai Xu +2
Autonomous vehicles in interactive traffic environments are often limited by the scarcity of safety-critical tail events in static datasets, which biases learned policies toward av…
A Knowledge-Driven Diffusion Policy for End-to-End Autonomous Driving Based on Expert Routing
Chengkai Xu, Jiaqi Liu, Yicheng Guo +2
End-to-end autonomous driving remains constrained by the difficulty of producing adaptive, robust, and interpretable decision-making across diverse scenarios. Existing methods ofte…
Interactive Adversarial Testing of Autonomous Vehicles with Adjustable Confrontation Intensity
Yicheng Guo, Chengkai Xu, Jiaqi Liu +3
Scientific testing techniques are essential for ensuring the safe operation of autonomous vehicles (AVs), with high-risk, highly interactive scenarios being a primary focus. To add…
Towards Human-Centric Autonomous Driving: A Fast-Slow Architecture Integrating Large Language Model Guidance with Reinforcement Learning
Chengkai Xu, Jiaqi Liu, Yicheng Guo +3
Autonomous driving has made significant strides through data-driven techniques, achieving robust performance in standardized tasks. However, existing methods frequently overlook us…
MAPPO-PIS: A Multi-Agent Proximal Policy Optimization Method with Prior Intent Sharing for CAVs' Cooperative Decision-Making
Yicheng Guo, Jiaqi Liu, Rongjie Yu +2
Vehicle-to-Vehicle (V2V) technologies have great potential for enhancing traffic flow efficiency and safety. However, cooperative decision-making in multi-agent systems, particular…