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

• URL: http://arxiv.org/abs/2402.12289v5
• Date: Tue, 25 Jun 2024 17:55:35 GMT
• Title: DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
• Authors: Xiaoyu Tian, Junru Gu, Bailin Li, Yicheng Liu, Yang Wang, Zhiyong Zhao, Kun Zhan, Peng Jia, Xianpeng Lang, Hang Zhao,
• Abstract summary: We introduce DriveVLM, an autonomous driving system leveraging Vision-Language Models (VLMs) DriveVLM integrates a unique combination of reasoning modules for scene description, scene analysis, and hierarchical planning. We propose DriveVLM-Dual, a hybrid system that synergizes the strengths of DriveVLM with the traditional autonomous driving pipeline.
• Score: 31.552397390480525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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