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

TL;DR

LimSim++ is a comprehensive, open-source simulation platform designed for deploying and evaluating multimodal large language models in autonomous driving, supporting continuous learning and multi-scenario testing.

Contribution

The paper presents LimSim++, an extended simulation platform enabling long-term, multi-scenario testing of (M)LLMs in autonomous driving, along with a baseline framework for evaluation.

Findings

Validated through quantitative experiments across diverse scenarios.

Supports prompt engineering, model evaluation, and framework enhancement.

Addresses limitations of existing simulation platforms for autonomous driving.

Abstract

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/.