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cs.RO2026
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…
cs.AI2026
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…
cs.RO2026
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…