Drive As You Like: Strategy-Level Motion Planning Based on A Multi-Head Diffusion Model

• URL: http://arxiv.org/abs/2508.16947v1
• Date: Sat, 23 Aug 2025 08:33:11 GMT
• Title: Drive As You Like: Strategy-Level Motion Planning Based on A Multi-Head Diffusion Model
• Authors: Fan Ding, Xuewen Luo, Hwa Hui Tew, Ruturaj Reddy, Xikun Wang, Junn Yong Loo,
• Abstract summary: We propose a diffusion-based multi-head trajectory planner(M-diffusion planner)<n>During the early training stage, all output heads share weights to learn to generate high-quality trajectories.<n>We incorporate a large language model (LLM) to guide strategy selection, enabling dynamic, instruction-aware planning.
• Score: 7.3078271605135114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in motion planning for autonomous driving have led to models capable of generating high-quality trajectories. However, most existing planners tend to fix their policy after supervised training, leading to consistent but rigid driving behaviors. This limits their ability to reflect human preferences or adapt to dynamic, instruction-driven demands. In this work, we propose a diffusion-based multi-head trajectory planner(M-diffusion planner). During the early training stage, all output heads share weights to learn to generate high-quality trajectories. Leveraging the probabilistic nature of diffusion models, we then apply Group Relative Policy Optimization (GRPO) to fine-tune the pre-trained model for diverse policy-specific behaviors. At inference time, we incorporate a large language model (LLM) to guide strategy selection, enabling dynamic, instruction-aware planning without switching models. Closed-loop simulation demonstrates that our post-trained planner retains strong planning capability while achieving state-of-the-art (SOTA) performance on the nuPlan val14 benchmark. Open-loop results further show that the generated trajectories exhibit clear diversity, effectively satisfying multi-modal driving behavior requirements. The code and related experiments will be released upon acceptance of the paper.

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