5 papers · 1 filter
A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
Chengkai Xu, Yiming Cui, Jiaqi Liu +7
Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative paradigm that directly maps m…
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…
A Vehicle-Infrastructure Multi-layer Cooperative Decision-making Framework
Yiming Cui, Shiyu Fang, Peng Hang +1
Autonomous driving has entered the testing phase, but due to the limited decision-making capabilities of individual vehicle algorithms, safety and efficiency issues have become mor…
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…