AI RESEARCH
Driving with A Thousand Faces: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
arXiv CS.CV
•
ArXi:2602.18757v2 Announce Type: replace Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single average driving style, neglecting individual differences. Achieving personalized E2E-AD faces challenges across three levels: limited real-world datasets with individual-level annotations, a lack of quantitative metrics for evaluating personal driving styles, and the absence of algorithms that can learn stylized representations from users' trajectories.