10 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…
Large Language Model based Interactive Decision-Making for Autonomous Driving
Xinwei Dong, Jiyang Li, Jiabin Xie +5
In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack pro…
Toward Cooperative Driving in Mixed Traffic: An Adaptive Potential Game-Based Approach with Field Test Verification
Shiyu Fang, Xiaocong Zhao, Xuekai Liu +4
Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficien…
VP-AutoTest: A Virtual-Physical Fusion Autonomous Driving Testing Platform
Yiming Cui, Shiyu Fang, Jiarui Zhang +6
The rapid development of autonomous vehicles has led to a surge in testing demand. Traditional testing methods, such as virtual simulation, closed-course, and public road testing,…
CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine
Shiyu Fang, Yiming Cui, Haoyang Liang +3
Autonomous Driving (AD) systems have made notable progress, but their performance in long-tail, safety-critical scenarios remains limited. These rare cases contribute a disproporti…
Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework
Shiyu Fang, Jiaqi Liu, Mingyu Ding +4
At present, Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not sat…