DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model

作者：Zhenhua Xu, Yujia Zhang, Enze Xie, Zhao Zhen, Yong Guo, Kenneth K. Wong, Zhenguo Li, Hengshuang Zhao · 发表于：IEEE Robotics and Automation Letters · 年份：2024 · DOI：10.1109/lra.2024.3440097 · 被引用次数：404 · 研究领域：Advanced Neural Network Applications、Topic Modeling、Multimodal Machine Learning Applications

Multimodallarge language models (MLLMs) have emerged as a prominent area of interest within the research community, given their proficiency in handling and reasoning with non-textual data, including images and videos. This study seeks to extend the application of MLLMs to the realm of autonomous driving by introducing DriveGPT4, a novel interpretable end-to-end autonomous driving system based on LLMs. Capable of processing multi-frame video inputs and textual queries, DriveGPT4 facilitates the interpretation of vehicle actions, offers pertinent reasoning, and effectively addresses a diverse range of questions posed by users. Furthermore, DriveGPT4 predicts low-level vehicle control signals in an end-to-end fashion. These advanced capabilities are achieved through the utilization of a bespoke visual instruction tuning dataset, specifically tailored for autonomous driving applications, in conjunction with a mix-finetuning training strategy. DriveGPT4 represents the pioneering effort to leverage LLMs for the development of an interpretable end-to-end autonomous driving solution. Evaluations conducted on the BDD-X dataset showcase the superior qualitative and quantitative performance of DriveGPT4. Additionally, the fine-tuning of domain-specific data enables DriveGPT4 to yield close or even improved results in terms of autonomous driving grounding when contrasted with GPT4-V.