GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories
  Generation in End-to-End Autonomous Driving
Authors: name: Zebin Xing, name: Xingyu Zhang, name: Yang Hu, name: Bo Jiang, name: Tong He, name: Qian Zhang, name: Xiaoxiao Long, name: Wei Yin

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.