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arXiv · 2402.12289

DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

Xiaoyu Tian·Junru Gu·Bailin Li·Yicheng Liu·Yang Wang·Zhiyong Zhao·Kun Zhan·Peng Jia·Xianpeng Lang·Hang Zhao
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Abstract

A primary hurdle of autonomous driving in urban environments is understanding complex and long-tail scenarios, such as challenging road conditions and delicate human behaviors. We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) for enhanced scene understanding and planning capabilities. DriveVLM integrates a unique combination of reasoning modules for scene description, scene analysis, and hierarchical planning. Furthermore, recognizing the limitations of VLMs in spatial reasoning and heavy computational requirements, we propose DriveVLM-Dual, a hybrid system that synergizes the strengths of DriveVLM with the traditional autonomous driving pipeline. Experiments on both the nuScenes dataset and our SUP-AD dataset demonstrate the efficacy of DriveVLM and DriveVLM-Dual in handling complex and unpredictable driving conditions. Finally, we deploy the DriveVLM-Dual on a production vehicle, verifying it is effective in real-world autonomous driving environments.

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Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao. 2024-02-19. DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models. https://arxiv.org/abs/2402.12289

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