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

arXiv:2508.16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving. Moreover, existing systems struggle to understand user intent from human interactions and environmental contexts. In real-world advanced deployment, motion planning must accommodate diverse, context-dependent user preferences to support heterogeneous driving services, requiring the ability to interpret human intent and adapt behavior accordingly. However, existing approaches lack such user-oriented capabilities, as they neither explicitly model user intent nor enable flexible policy adaptation. To bridge this gap, we propose an RL-guided multi-strategy framework with a diffusion-based multi-head planner(M-Diffusion Planner) integrated with LLM-based semantic understanding, enabling dynamic perception of user intent and generation of diverse, preference-aligned trajectories. To balance trajectory quality and strategy alignment, we adopt a two-stage training paradigm: first, imitation learning ensures each policy head achieves safe and high-quality planning; second, constrained Group Relative Policy Optimization (GRPO) further aligns each head with user preferences. Experiments on the nuPlan benchmark, under both open-loop and closed-loop settings, demonstrate competitive performance while meeting real-time planning requirements and effectively aligning with user intent.

Abstract:Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving. Moreover, existing systems struggle to understand user intent from human interactions and environmental contexts. In real-world advanced deployment, motion planning must accommodate diverse, context-dependent user preferences to support heterogeneous driving services, requiring the ability to interpret human intent and adapt behavior accordingly. However, existing approaches lack such user-oriented capabilities, as they neither explicitly model user intent nor enable flexible policy adaptation. To bridge this gap, we propose an RL-guided multi-strategy framework with a diffusion-based multi-head planner(M-Diffusion Planner) integrated with LLM-based semantic understanding, enabling dynamic perception of user intent and generation of diverse, preference-aligned trajectories. To balance trajectory quality and strategy alignment, we adopt a two-stage training paradigm: first, imitation learning ensures each policy head achieves safe and high-quality planning; second, constrained Group Relative Policy Optimization (GRPO) further aligns each head with user preferences. Experiments on the nuPlan benchmark, under both open-loop and closed-loop settings, demonstrate competitive performance while meeting real-time planning requirements and effectively aligning with user intent.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2508.16947 [cs.RO]

(or arXiv:2508.16947v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2508.16947 Focus to learn more arXiv-issued DOI via DataCite

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

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