3 papers
cs.CV2026
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…
cs.RO2026
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…
cs.RO2026
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…