LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving

The emergence of Multimodal Large Language Models ((M)LLMs) has ushered in new avenues in artificial intelligence, particularly for autonomous driving by offering enhanced understanding and reasoning capabilities. This paper introduces LimSim++, an extended version of LimSim designed for the applica...

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Bibliographic Details
Published in2024 IEEE Intelligent Vehicles Symposium (IV) pp. 1084 - 1090
Main Authors Fu, Daocheng, Lei, Wenjie, Wen, Licheng, Cai, Pinlong, Mao, Song, Dou, Min, Shi, Botian, Qiao, Yu
Format Conference Proceeding
LanguageEnglish
Published IEEE 02.06.2024
Subjects
Autonomous vehicles
Cognition
Decision making
Large language models
Prompt engineering
Random access memory
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Summary:The emergence of Multimodal Large Language Models ((M)LLMs) has ushered in new avenues in artificial intelligence, particularly for autonomous driving by offering enhanced understanding and reasoning capabilities. This paper introduces LimSim++, an extended version of LimSim designed for the application of (M)LLMs in autonomous driving. Acknowledging the limitations of existing simulation platforms, LimSim++ addresses the need for a long-term closed-loop infrastructure supporting continuous learning and improved generalization in autonomous driving. The platform offers extended-duration, multi-scenario simulations, providing crucial information for (M)LLM-driven vehicles. Users can engage in prompt engineering, model evaluation, and framework enhancement, making LimSim++ a versatile tool for research and practice. This paper additionally introduces a baseline (M)LLM-driven framework, systematically validated through quantitative experiments across diverse scenarios. The open-source resources of LimSim++ are available at: https://pjlab-adg.github.io/limsim-plus/.
ISSN:2642-7214
DOI:10.1109/IV55156.2024.10588848