GoalFlow：面向端到端自动驾驶的多模态轨迹生成之目标驱动流匹配

GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving

March 7, 2025

作者: Zebin Xing, Xingyu Zhang, Yang Hu, Bo Jiang, Tong He, Qian Zhang, Xiaoxiao Long, Wei Yin

cs.AI

摘要

我們提出GoalFlow，一種端到端的自動駕駛方法，用於生成高質量的多模態軌跡。在自動駕駛場景中，很少存在單一合適的軌跡。最近的方法越來越注重建模多模態軌跡分佈。然而，由於軌跡的高發散性以及引導信息與場景信息之間的不一致性，這些方法面臨軌跡選擇複雜性和軌跡質量下降的問題。為了解決這些問題，我們引入了GoalFlow，這是一種新穎的方法，能夠有效約束生成過程以產生高質量的多模態軌跡。為了解決基於擴散方法固有的軌跡發散問題，GoalFlow通過引入目標點來約束生成的軌跡。GoalFlow建立了一種新穎的評分機制，根據場景信息從候選點中選擇最合適的目標點。此外，GoalFlow採用高效的生成方法Flow Matching來生成多模態軌跡，並結合精煉的評分機制從候選軌跡中選擇最佳軌跡。我們在NavsimDauner2024_navsim上驗證的實驗結果表明，GoalFlow實現了最先進的性能，為自動駕駛提供了穩健的多模態軌跡。GoalFlow的PDMS達到了90.3，顯著超越了其他方法。與其他基於擴散策略的方法相比，我們的方法僅需一次去噪步驟即可獲得優異的性能。代碼可在https://github.com/YvanYin/GoalFlow獲取。

English

We propose GoalFlow, an end-to-end autonomous driving method for generating high-quality multimodal trajectories. In autonomous driving scenarios, there is rarely a single suitable trajectory. Recent methods have increasingly focused on modeling multimodal trajectory distributions. However, they suffer from trajectory selection complexity and reduced trajectory quality due to high trajectory divergence and inconsistencies between guidance and scene information. To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories. To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point. GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information. Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the candidates. Our experimental results, validated on the NavsimDauner2024_navsim, demonstrate that GoalFlow achieves state-of-the-art performance, delivering robust multimodal trajectories for autonomous driving. GoalFlow achieved PDMS of 90.3, significantly surpassing other methods. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance. The code is available at https://github.com/YvanYin/GoalFlow.