Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving
arXiv:2508.16947v3 Announce Type: replace-cross Abstract: Personalized autonomous driving requires understanding the driving needs implicit in user requests. This is challenging when driving demonstrations lack style annotations and behavioral adaptation risks degrading planning quality. We propose M-Diffusion Planner with a dual-decoupling design: separating intent understanding from trajectory planning, and general planning capability learning from strategy alignment. A large language model i…