Add model card for SparseDriveV2

README.md

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+---
+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](https://huggingface.co/papers/2603.29163)
+- **GitHub Repository:** [swc-17/SparseDriveV2](https://github.com/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
+
+```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}
+}
+```