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LMDrive: Closed-Loop End-to-End Driving with Large Language Models

Hao Shao · Yuxuan Hu · Letian Wang · Guanglu Song · Steven L. Waslander · Yu Liu · Hongsheng Li

Arch 4A-E Poster #50
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Thu 20 Jun 5 p.m. PDT — 6:30 p.m. PDT

Abstract: Despite significant recent progress in the field of autonomous driving, modern methods still struggle and can incur serious accidents when encountering long-tail unforeseen events and challenging urban scenarios. On the one hand, large language models (LLM) have shown impressive reasoning capabilities that approach “Artificial General Intelligence”. On the other hand, previous autonomous driving methods tend to rely on limited-format inputs ($\textit{e.g.}$, sensor data and navigation waypoints), restricting the vehicle's ability to understand language information and interact with humans. To this end, this paper introduces LMDrive, a novel language-guided, end-to-end, closed-loop autonomous driving framework. LMDrive uniquely processes and integrates multi-modal sensor data with natural language instructions, enabling interaction with humans and navigation software in realistic instructional settings. To facilitate further research in language-based closed-loop autonomous driving, we also publicly release the corresponding dataset which includes approximately 64K instruction-following data clips, and the LangAuto benchmark that tests the system's ability to handle complex instructions and challenging driving scenarios. Extensive closed-loop experiments are conducted to demonstrate LMDrive's effectiveness. To the best of our knowledge, we're the very first work to leverage LLMs for closed-loop end-to-end autonomous driving. More demo videos and codes can be found at our webpage:

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