Driving like yourself: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving

Human driving behavior is inherently diverse, yet most end-to-end autonomous driving (E2E-AD) systems learn a single averagedriving style, neglecting individual differences. Achieving personalizedE2E-AD faces challenges across three levels: limited real-world datasetswith individual-level annotations, a lack of quantitative metrics for eval-uating personal driving styles, and the absence of algorithms that canlearn stylized representations from users’ trajectories. To address thesegaps, we propose Person2Drive, a comprehensive personalized E2E-ADplatform and benchmark. It includes an open-source, flexible data col-lection system that simulates realistic scenarios to generate scalable, di-verse personalized driving datasets; style vector–based evaluation metricswith Maximum Mean Discrepancy and KL divergence to comprehen-sively quantify individual driving behaviors; and a personalized E2E-ADframework with a style reward model that efficiently adapts E2E mod-els for safe and individualized driving. Crucially, our framework enablesplug-and-play personalization by fine-tuning only the trajectory predic-tion head, preserving the pretrained base model and ensuring safety. Ex-tensive experiments demonstrate that Person2Drive enables fine-grainedanalysis and effective personalization, while preserving driving perfor-mance and success rate even in challenging scenarios.