title: GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
    Generation in End-to-End Autonomous Driving
summary: 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.
paper_authors._id: 67d37c5c3b54e330517a545d
paper_authors.name: Zebin Xing
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:56:46.875Z
paper_authors._id: 665b2ac6e0e2374ca24ba000
paper_authors.avatarUrl: /avatars/d5218c9fa3dceae7b91df2e1d396bcf3.svg
paper_authors.fullname: Zebin Xing
paper_authors.type: user
paper_authors.user: XXXXing
paper_authors._id: 67d37c5c3b54e330517a545e
paper_authors.name: Xingyu Zhang
paper_authors._id: 67d37c5c3b54e330517a545f
paper_authors.name: Yang Hu
paper_authors._id: 67d37c5c3b54e330517a5460
paper_authors.name: Bo Jiang
paper_authors._id: 67d37c5c3b54e330517a5461
paper_authors.name: Tong He
paper_authors._id: 67d37c5c3b54e330517a5462
paper_authors.name: Qian Zhang
paper_authors._id: 67d37c5c3b54e330517a5463
paper_authors.name: Xiaoxiao Long
paper_authors._id: 67d37c5c3b54e330517a5464
paper_authors.name: Wei Yin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:44:54.732Z
paper_authors._id: 654a2b1a83e7bfc4313a5cc7
paper_authors.avatarUrl: /avatars/dc3dfc3fcd26bb7350a9db0d075c5ea0.svg
paper_authors.fullname: Wei Yin
paper_authors.type: user
paper_authors.user: WonderingWorld
paper_title: GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
    Generation in End-to-End Autonomous Driving
paper_summary: 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.
paper_ai_keywords: GoalFlow multimodal trajectories trajectory selection complexity trajectory divergence diffusion-based methods goal point scoring mechanism Flow Matching Navsim PDMS diffusion-policy-based methods denoising step