GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
  Generation in End-to-End Autonomous Driving
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.
67d37c5c3b54e330517a545d
Zebin Xing
claimed_verified
2025-03-14T08:56:46.875Z
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Zebin Xing
user
XXXXing
67d37c5c3b54e330517a545e
Xingyu Zhang
67d37c5c3b54e330517a545f
Yang Hu
67d37c5c3b54e330517a5460
Bo Jiang
67d37c5c3b54e330517a5461
Tong He
claimed_verified
2025-07-17T13:57:16.716Z
6747ede3a9c72aebe1322382
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/inILqQ05sESbYLdsEldJ_.png
Tong He
user
tonghe90
67d37c5c3b54e330517a5462
Qian Zhang
67d37c5c3b54e330517a5463
Xiaoxiao Long
67d37c5c3b54e330517a5464
Wei Yin
claimed_verified
2025-03-17T08:44:54.732Z
654a2b1a83e7bfc4313a5cc7
/avatars/dc3dfc3fcd26bb7350a9db0d075c5ea0.svg
Wei Yin
user
WonderingWorld
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
  Generation in End-to-End Autonomous Driving
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.
GoalFlow is an autonomous driving method that generates high-quality multimodal trajectories using a goal point and Flow Matching, achieving state-of-the-art performance with efficient trajectory selection.
GoalFlow multimodal trajectories diffusion-based methods trajectory divergence goal point scoring mechanism Flow Matching Navsim PDMS