3 citations · 11 across the 25 of their papers we have counts for
7 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…
DriveHierarchy: A Benchmark for Diagnosing VLM Driving Capabilities from Open-Loop Understanding to Closed-Loop Execution
Chengkai Xu, Jiaqi Liu, Yicheng Guo +2
Evaluating VLM-based autonomous driving remains difficult because driving competence is composite, where a capable system must ground traffic participants and hazards, integrate co…
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…
OVPD: A Virtual-Physical Fusion Testing Dataset of OnSite Auton-omous Driving Challenge
Yuhang Zhang, Jiarui Zhang, Bowen Jian +6
The rapid iteration of autonomous driving algorithms has created a growing demand for high-fidelity, replayable, and diagnosable testing data. However, many public datasets lack re…
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…
Adaptive Bounded-Rationality Modeling of Early-Stage Takeover in Shared-Control Driving
Jian Sun, Xiyan Jiang, Xiaocong Zhao +3
Human drivers' control quality in the first seconds after a handover is critical to shared-driving safety; potentially unsafe steering or pedal inputs therefore require detection a…
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…