Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling

• URL: http://arxiv.org/abs/2510.11083v1
• Date: Mon, 13 Oct 2025 07:25:13 GMT
• Title: Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
• Authors: Tianyi Tan, Yinan Zheng, Ruiming Liang, Zexu Wang, Kexin Zheng, Jinliang Zheng, Jianxiong Li, Xianyuan Zhan, Jingjing Liu,
• Abstract summary: Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning.<n>We propose Flow Planner, which tackles these problems through coordinated innovations in data modeling, model architecture, and learning scheme.<n>Flow Planner achieves state-of-the-art performance among learning-based approaches while effectively modeling interactive behaviors in complex driving scenarios.
• Score: 26.71028572181775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. Learning-based approaches attempt to address this challenge with advanced generative models, removing the dependency on over-engineered architectures for representation fusion. However, brute-force implementation by simply stacking transformer blocks lacks a dedicated mechanism for modeling interactive behaviors that are common in real driving scenarios. The scarcity of interactive driving data further exacerbates this problem, leaving conventional imitation learning methods ill-equipped to capture high-value interactive behaviors. We propose Flow Planner, which tackles these problems through coordinated innovations in data modeling, model architecture, and learning scheme. Specifically, we first introduce fine-grained trajectory tokenization, which decomposes the trajectory into overlapping segments to decrease the complexity of whole trajectory modeling. With a sophisticatedly designed architecture, we achieve efficient temporal and spatial fusion of planning and scene information, to better capture interactive behaviors. In addition, the framework incorporates flow matching with classifier-free guidance for multi-modal behavior generation, which dynamically reweights agent interactions during inference to maintain coherent response strategies, providing a critical boost for interactive scenario understanding. Experimental results on the large-scale nuPlan dataset and challenging interactive interPlan dataset demonstrate that Flow Planner achieves state-of-the-art performance among learning-based approaches while effectively modeling interactive behaviors in complex driving scenarios.

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