SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
License: MIT License
Shell 0.34%C++ 0.84%Python 96.86%Cuda 1.96%
sparsedrive's Introduction
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
vis_sparsedrive.mp4
News
24 June, 2024: We reorganize code for better readability. Code & Models are released.
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.
We found that some collision cases were not taken into consideration in our previous code, so we re-implement the evaluation metric for collision rate in released code and provide updated results.
If you find SparseDrive useful in your research or applications, please consider giving us a star ๐ 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}
}