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/.

翻译：多模态大语言模型（(M)LLMs）的出现为人工智能领域注入了新动力，尤其在自动驾驶方面，因其提供了更强的理解与推理能力。本文介绍了LimSim++，这是LimSim的扩展版本，专为(M)LLMs在自动驾驶中的应用而设计。鉴于现有仿真平台的局限性，LimSim++解决了自动驾驶中需要长期闭环基础设施以支持持续学习和提升泛化能力的需求。该平台支持长时间跨度、多场景的仿真，为(M)LLM驱动的车辆提供关键信息。用户可参与提示工程、模型评估及框架优化，使LimSim++成为研究和实践的通用工具。本文还引入了一个基于(M)LLM的基准框架，并通过跨多样化场景的定量实验进行了系统验证。LimSim++的开源资源可访问：https://pjlab-adg.github.io/limsim_plus/。