EvoDriveVLA

<div align="center"> <a id="readme-top"></a> <h1> <img style="vertical-align: -10px;" :height="50px" width="50px"> EvoDriveVLA: Evolving Autonomous Driving VLA Models via Collaborative Perception-Planning Distillation </h1> <!-- <h3 align="center"><strong>🎉🎉NeurIPS 2025 spotlight🎉🎉</strong></h3> --> <!-- <a href="https://arxiv.org/abs/2505.17685"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a> <a href='https://miv-xjtu.github.io/FSDrive.github.io'><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=googlechrome&logoColor=white' alt='Project Page'></a> -->

Jiajun Cao^1,2†, Xiaoan Zhang^1,2†, Xiaobao Wei^1†, Liyuqiu Huang^1,2, Wang Zijian², Hanzhen Zhang², Zhengyu Jia², Wei Mao², Xianming Liu², Shuchang Zhou², Yang Wang^2*, Shanghang Zhang^1*,

¹Peking University, ²XPENG

† Equal contribution

* Corresponding authors

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Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the visual encoder and struggle with accumulated instability in long-term planning. To address these challenges, we propose EvoDriveVLA a novel collaborative perception-planning distillation framework that integrates self-anchored perceptual constraints and oracle-guided trajectory optimization. Specifically, self-anchored visual distillation leverages self-anchor teacher to deliver visual anchoring constraints, regularizing student representations via trajectory-guided key-region awareness. In parallel, oracle-guided trajectory distillation employs a future-aware oracle-teacher with coarse-to-fine trajectory refinement and Monte Carlo dropout sampling to produce high-quality trajectory candidates, thereby selecting the optimal trajectory to guide the student’s prediction.

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📜 Citing

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

<!-- ``` @article{zeng2025futuresightdrive, title={FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving}, author={Zeng, Shuang and Chang, Xinyuan and Xie, Mengwei and Liu, Xinran and Bai, Yifan and Pan, Zheng and Xu, Mu and Wei, Xing}, journal={arXiv preprint arXiv:2505.17685}, year={2025} } ``` --> <p align="right"><a href="#readme-top"><img src=https://img.shields.io/badge/back%20to%20top-red?style=flat ></a></p>

🙏 Acknowledgement

Our work is primarily based on the following codebases:Impromptu-VLA, FSDrive and, OmniDrive.

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