VLM-Auto: VLM-based Autonomous Driving Assistant with Human-like Behavior and Understanding for Complex Road Scenes

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2022

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J. Cui, H. Qiu, D. Chen, P. Stone, and Y. Zhu, “Coopernaut: End-to-end driving with cooperative perception for networked vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 252–17 262

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2023

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H. Shao, L. Wang, R. Chen, S. L. Waslander, H. Li, and Y. Liu, “Reasonnet: End-to-end driving with temporal and global reasoning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 723–13 733

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Y. Wang, R. Jiao, C. Lang, S. S. Zhan, C. Huang, Z. Wang, Z. Yang, and Q. Zhu, “Empowering autonomous driving with large language models: A safety perspective,” arXiv preprint arXiv:2312.00812 , 2023

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W. Han, D. Guo, C.-Z. Xu, and J. Shen, “Dme-driver: Integrating human decision logic and 3d scene perception in autonomous driving,” arXiv preprint arXiv:2401.03641 , 2024

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2024

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A. Lykov, M. Litvinov, M. Konenkov, R. Prochii, N. Burtsev, A. A. Abdulkarim, A. Bazhenov, V. Berman, and D. Tsetserukou, “Cognitivedog: Large multimodal model based system to translate vision and language into action of quadruped robot,” in Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction , 2024, pp. 712–716

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B. Li, Y. Wang, J. Mao, B. Ivanovic, S. Veer, K. Leung, and M. Pavone, “Driving everywhere with large language model policy adaptation,” arXiv preprint arXiv:2402.05932 , 2024

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