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Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving
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: separat
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