5 papers
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
Xuerun Yan, Zhexi Lian, Nuoheng Zhang +5
Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving. However, existing methods either lack comprehensive world cognition or suffer from fragmented world…
Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving
Zhexi Lian, Haoran Wang, Xuerun Yan +4
End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is constrained by the quality of human demonstrations. To overcome this limitatio…
MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
Jia Hu, Zhexi Lian, Xuerun Yan +5
Autonomous Driving (AD) vehicles still struggle to exhibit human-like behavior in highly dynamic and interactive traffic scenarios. The key challenge lies in AD's limited ability t…
A simulation platform calibration method for automated vehicle evaluation: accurate on both vehicle level and traffic flow level
Jia Hu, Junqi Li, Xuerun Yan +2
Simulation testing is a fundamental approach for evaluating automated vehicles (AVs). To ensure its reliability, it is crucial to accurately replicate interactions between AVs and…
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
Jia Hu, Xuerun Yan, Tian Xu +1
Reinforcement Learning (RL) offers a promising solution to enable evolutionary automated driving. However, the conventional RL method is always concerned with risk performance. The…