Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving
[Bo Jiang](https://scholar.google.com/citations?user=UlDxGP0AAAAJ&hl=zh-CN)1, [Shaoyu Chen](https://scholar.google.com/citations?user=PIeNN2gAAAAJ&hl=en&oi=sra)1, [Bencheng Liao](https://scholar.google.com/citations?user=rUBdh_sAAAAJ&hl=zh-CN)1, Xingyu Zhang2, Wei Yin2, [Qian Zhang](https://scholar.google.com/citations?user=pCY-bikAAAAJ&hl=zh-CN)2, [Chang Huang](https://scholar.google.com/citations?user=IyyEKyIAAAAJ&hl=zh-CN)2, [Wenyu Liu](http://eic.hust.edu.cn/professor/liuwenyu/)1, [Xinggang Wang](https://xwcv.github.io/)1,📧 1 Huazhong University of Science and Technology, 2 Horizon Robotics, 📧 Corresponding Author [](https://arxiv.org/abs/2410.22313)
News
[2024-10-04]: Senna arXiv paper released. Code/Models are coming soon. Please stay tuned! ☕️
Highlights
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Senna is an autonomous driving system that integrates a Large Vision-Language Model with an end-to-end model to improve planning safety, robustness and generalization.
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Senna achieves SOTA planning performance and demonstrates strong cross-scenario generalization and transferability.
Visualizations
Acknowledgments
LLaVA, the codebase we built upon, we sincerely thank the contributors for their great work!
Citation
If you find Senna useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@article{jiang2024senna,
title={Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving},
author={Bo Jiang and Shaoyu Chen and Bencheng Liao and Xingyu Zhang and Wei Yin and Qian Zhang and Chang Huang and Wenyu Liu and Xinggang Wang},
year={2024},
eprint={2410.22313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.22313},
}