LimSim++：マルチモーダルLLMを自動運転に展開するための閉ループプラットフォーム
LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving
マルチモーダル大規模言語モデルを自動運転に応用するため、長時間・多シナリオの閉ループシミュレーションを可能にする拡張プラットフォームLimSim++を提案し、ベースラインの(M)LLM駆動フレームワークを実験的に検証した。
著者: Daocheng Fu, Wenjie Lei, Licheng Wen, Pinlong Cai, Song Mao, Min Dou, Botian Shi, Yu Qiao
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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/.
