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 infers driving needs and selects a strategy, which is executed by the corresponding head of a shared d

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