2024/02/02 by Daocheng Fu, Wenjie Lei, Fu, Daocheng +13 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Multi-Agent Systems and Negotiation #Robotics (cs.RO) #Speech and dialogue systems #Systems and Control (eess.SY) #Transportation and Mobility Innovations #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.01246
openalex publication_date 2024/02/02 · openalex created_date 2024/02/06 · openalex updated_date 2026/07/28
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/.