扩散规划器全新升级！清华Flow Planner：基于流匹配模型的博弈增强算法（NeurIPS'25）

Core Insights - The article presents a new autonomous driving decision-making algorithm framework called Flow Planner, which improves upon the existing Diffusion Planner by effectively modeling advanced interactive behaviors in high-density traffic scenarios [1][4][22]. Group 1: Background and Challenges - One of the core challenges in autonomous driving planning is achieving safe and reliable human-like decision-making in dense and diverse traffic environments [3]. - Traditional rule-based methods lack generalization capabilities in dynamic traffic games, while learning-based methods struggle with limited high-quality training data and the need for effective game behavior modeling [6][8]. Group 2: Innovations of Flow Planner - Flow Planner introduces three key innovations: fine-grained trajectory tokenization, interaction-enhanced spatiotemporal fusion, and classifier-free guidance for trajectory generation [4][23]. - Fine-grained trajectory tokenization allows for better representation of trajectories by dividing them into overlapping segments, improving coherence and diversity in planning [8]. - The interaction-enhanced spatiotemporal fusion mechanism enables the model to effectively capture spatial interactions and temporal consistency among various traffic participants [9][13]. - Classifier-free guidance allows for flexible adjustment of model sampling distributions during inference, enhancing the generation of driving behaviors and strategies [10]. Group 3: Experimental Results - Flow Planner achieved state-of-the-art (SOTA) performance on the nuPlan benchmark, surpassing 90 points on the Val14 benchmark without relying on any rule-based prior or post-processing modules [11][14]. - In the newly proposed interPlan benchmark, Flow Planner significantly outperformed other baseline methods, demonstrating superior response strategies in high-density traffic and pedestrian crossing scenarios [15][20]. Group 4: Conclusion - The Flow Planner framework significantly enhances decision-making performance in complex traffic interactions through its innovative modeling approaches, showcasing strong potential for adaptability across various scenarios [22][23].