README.md

metadata

pipeline_tag: robotics

SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving

SparseDriveV2 is an end-to-end multi-modal planning framework for autonomous driving. It demonstrates that performance consistently improves as trajectory anchors become denser, achieving state-of-the-art results through a scalable vocabulary representation and a factorized scoring strategy.

• Paper: SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
• GitHub Repository: swc-17/SparseDriveV2

Method Overview

SparseDriveV2 pushes the performance boundary of scoring-based planning through two complementary innovations:

1. Scalable Vocabulary Representation: A factorized structure that decomposes trajectories into geometric paths and velocity profiles, enabling combinatorial coverage of the action space.
2. Scalable Scoring Strategy: A coarse factorized scoring over paths and velocity profiles followed by fine-grained scoring on a small set of composed trajectories.

This approach allows the model to scale its trajectory vocabulary to be 32x denser than prior methods while maintaining computational efficiency.

Performance

The model achieves state-of-the-art performance using a lightweight ResNet-34 backbone:

Benchmark	Metric	Score
NAVSIM	PDMS	92.0
NAVSIM	EPDMS	90.1
Bench2Drive	Driving Score	89.15
Bench2Drive	Success Rate	70.00

Citation

@article{sun2026sparsedrivev2,
  title={SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving},
  author={Sun, Wenchao and Lin, Xuewu and Chen, Keyu and Pei, Zixiang and Li, Xiang and Shi, Yining and Zheng, Sifa},
  journal={arXiv preprint arXiv:2603.29163},
  year={2026}
}