license: apache-2.0
pipeline_tag: robotics
library_name: diffusers
tags:
- autonomous-driving
- trajectory-generation
- flow-matching
Paper | Weight | ProjectPage | Code
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving
Zebin Xing1,2*, Xingyu Zhang2*, Yang Hu1,2, Bo Jiang4,2, Tong He5, Qian Zhang2, Xiaoxiao Long3, Wei Yin2✝
1 University of Chinese Academy of Sciences, 2 Horizon Robotics, 3 Nanjing University, 4 Huazhong University of Science & Technology, 3 Shanghai AI Laboratory
Computer Vision and Pattern Recognition (CVPR), 2025
This is the official repo of 'GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving (CVPR 2025)'. GoalFlow achieved PDMS of 90.3, significantly surpassing other baselines. Compared with other diffusion-policy-based methods, our approach requires only a single denoising step to obtain excellent performance.
Abstract
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 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.
News
20 Mar, 2025: We released our paper on arXiv. Code is coming soon.27 Feb, 2025: GoalFlow was accepted at CVPR !
Introduction
In autonomous driving, multiple optimal trajectories exist, like overtaking or following. (1) Traditional methods efficiently generate safe trajectories but struggle with multimodal ones. (2) Generative methods like diffusion models capture multimodal distributions but require heavy hardware and prior information. We propose GoalFlow, a goal-point-based method that guides trajectory planning. With a map-free evaluation and an efficient diffusion variant, Flow Matching, we reduce inference steps, achieving superior performance with just one denoising step.
Visualization
Comparison with Other Methods
❌ indicates that the trajectory results in a collision or goes beyond the drivable area, while ✅ represents a safe trajectory. The orange points are optimal goal points evaluated by the Goal Constructor, while the blue and yellow points correspond to samples from the vocabulary.
Driving Vedios
Driving Vedios generated by GoalFlow.
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Goal Point Distribution
From top to down, they are respectively the distributions of DAC, distance, and the final score. The points with warmer color have higher score.
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Results
Planning results on the proposed NAVSIM Test benchmark. Please refer to the paper for more details.
| Method | SNC ↑ | SDAC ↑ | STTC ↑ | SCF ↑ | SEP ↑ | SPDM ↑ |
|---|---|---|---|---|---|---|
| Constant Velocity | 68.0 | 57.8 | 50.0 | 100 | 19.4 | 20.6 |
| Ego Status MLP | 93.0 | 77.3 | 83.6 | 100 | 62.8 | 65.6 |
| LTF | 97.4 | 92.8 | 92.4 | 100 | 79.0 | 83.8 |
| TransFuser | 97.7 | 92.8 | 92.8 | 100 | 79.2 | 84.0 |
| UniAD | 97.8 | 91.9 | 92.9 | 100 | 78.8 | 83.4 |
| PARA-Drive | 97.9 | 92.4 | 93.0 | 99.8 | 79.3 | 84.0 |
| GoalFlow (Ours) | 98.4 | 98.3 | 94.6 | 100 | 85.0 | 90.3 |
| Human‡ | 100 | 100 | 100 | 99.9 | 87.5 | 94.8 |
Getting started
Contact
If you have any questions or suggestions, please feel free to open an issue or contact us (xzebin@bupt.edu.cn).
Acknowledgement
1. We have gained valuable insights from Hydra-MDP, which provided many inspiring ideas referenced in our work.
2. We referred to an excellent GitHub project, tuplan garage, and incorporated aspects of its page design.
3. GoalFlow is also greatly inspired by the following outstanding contributions to the open-source community:
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NAVSIM | TransFuser | Diffusion-ES | VAD-v2
Citation
If you find GoalFlow useful, please consider giving us a star 🌟 and citing our paper with the following BibTeX entry.
@article{xing2025goalflow,
title={GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving},
author={Xing, Zebin and Zhang, Xingyu and Hu, Yang and Jiang, Bo and He, Tong and Zhang, Qian and Long, Xiaoxiao and Yin, Wei},
journal={arXiv preprint arXiv:2503.05689},
year={2025}}