/SparseDrive

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SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation

vis_sparsedrive.mp4

News

  • 31 May, 2024: We release the SparseDrive paper on arXiv. Code/Models will be released in June, 2024. Please stay tuned!

Introduction

SparseDrive is a Sparse-Centric paradigm for end-to-end autonomous driving.

  • We explore the sparse scene representation for end-to-end autonomous driving and propose a Sparse-Centric paradigm named SparseDrive, which unifies multiple tasks with sparse instance representation.
  • We revise the great similarity shared between motion prediction and planning, correspondingly leading to a parallel design for motion planner. We further propose a hierarchical planning selection strategy incorporating a collision-aware rescore module to boost the planning performance.
  • On the challenging nuScenes benchmark, SparseDrive surpasses previous SOTA methods in terms of all metrics, especially the safety-critical metric collision rate, while keeping much higher training and inference efficiency.


Overview of SparseDrive. SparseDrive first encodes multi-view images into feature maps, then learns sparse scene representation through symmetric sparse perception, and finally perform motion prediction and planning in a parallel manner. An instance memory queue is devised for temporal modeling.


Model architecture of symmetric sparse perception, which unifies detection, tracking and online mapping in a symmetric structure.


Model structure of parallel motion planner, which performs motion prediction and planning simultaneously and outputs safe planning trajectory.

Results

  • Comprehensive results for all tasks on nuScenes.
Method NDS AMOTA minADE (m) L2 (m) Avg Col. (%) Avg Training Time (h) FPS
UniAD 0.498 0.359 0.71 0.73 0.61 144 1.8
SparseDrive-S 0.525 0.386 0.62 0.61 0.08 20 9.0
SparseDrive-B 0.588 0.501 0.60 0.58 0.06 30 7.3
  • Open-loop planning results on nuScenes.
Method L2 (m) 1s L2 (m) 2s L2 (m) 3s L2 (m) Avg Col. (%) 1s Col. (%) 2s Col. (%) 3s Col. (%) Avg FPS
UniAD 0.45 0.70 1.04 0.73 0.62 0.58 0.63 0.61 1.8
VAD 0.41 0.70 1.05 0.72 0.03 0.19 0.43 0.21 4.5
SparseDrive-S 0.29 0.58 0.96 0.61 0.01 0.05 0.18 0.08 9.0
SparseDrive-B 0.29 0.55 0.91 0.58 0.01 0.02 0.13 0.06 7.3

Citation

If you find SparseDrive useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@article{sun2024sparsedrive,
  title={SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation},
  author={Sun, Wenchao and Lin, Xuewu and Shi, Yining and Zhang, Chuang and Wu, Haoran and Zheng, Sifa},
  journal={arXiv preprint arXiv:2405.19620},
  year={2024}
}

Acknowledgement