时隔一年DiffusionDrive升级到v2，创下了新纪录！

Core Insights - The article discusses the upgrade of DiffusionDrive to version 2, highlighting its advancements in end-to-end autonomous driving trajectory planning through the integration of reinforcement learning to address the challenges of diversity and sustained high quality in trajectory generation [1][3][10]. Background Review - The shift towards end-to-end autonomous driving (E2E-AD) has emerged as traditional tasks like 3D object detection and motion prediction have matured. Early methods faced limitations in modeling, often generating single trajectories without alternatives in complex driving scenarios [5][10]. - Previous diffusion models applied to trajectory generation struggled with mode collapse, leading to a lack of diversity in generated behaviors. DiffusionDrive introduced a Gaussian Mixture Model (GMM) to define prior distributions for initial noise, promoting diverse behavior generation [5][13]. Methodology - DiffusionDriveV2 introduces a novel framework that utilizes reinforcement learning to overcome the limitations of imitation learning, which previously led to a trade-off between diversity and sustained high quality in trajectory generation [10][12]. - The framework incorporates intra-anchor GRPO and inter-anchor truncated GRPO to manage advantage estimation within specific driving intentions, preventing mode collapse by avoiding inappropriate comparisons between different intentions [9][12][28]. - The method employs scale-adaptive multiplicative noise to enhance exploration while maintaining trajectory smoothness, addressing the inherent scale inconsistency between proximal and distal segments of trajectories [24][39]. Experimental Results - Evaluations on the NAVSIM v1 and NAVSIM v2 datasets demonstrated that DiffusionDriveV2 achieved state-of-the-art performance, with a PDMS score of 91.2 on NAVSIM v1 and 85.5 on NAVSIM v2, significantly outperforming previous models [10][33]. - The results indicate that DiffusionDriveV2 effectively balances trajectory diversity and sustained quality, achieving optimal performance in closed-loop evaluations [38][39]. Conclusion - The article concludes that DiffusionDriveV2 successfully addresses the inherent challenges of imitation learning in trajectory generation, achieving an optimal trade-off between planning quality and diversity through innovative reinforcement learning techniques [47].