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| pipeline_tag: robotics |
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| |
| # SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving |
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| 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. |
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| - **Paper:** [SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving](https://huggingface.co/papers/2603.29163) |
| - **GitHub Repository:** [swc-17/SparseDriveV2](https://github.com/swc-17/SparseDriveV2) |
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| ## Method Overview |
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| 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. |
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| This approach allows the model to scale its trajectory vocabulary to be 32x denser than prior methods while maintaining computational efficiency. |
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| ## Performance |
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| The model achieves state-of-the-art performance using a lightweight ResNet-34 backbone: |
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| | Benchmark | Metric | Score | |
| | :--- | :--- | :--- | |
| | **NAVSIM** | PDMS | 92.0 | |
| | **NAVSIM** | EPDMS | 90.1 | |
| | **Bench2Drive** | Driving Score | 89.15 | |
| | **Bench2Drive** | Success Rate | 70.00 | |
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| ## Citation |
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|
| ```bibtex |
| @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} |
| } |
| ``` |
