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Background

  • Background The paper introduces a significant wave in AI and autonomous driving with the advent of Multimodal Large Language Models ((M)LLMs), which offer enhanced understanding and reasoning capabilities. However, it highlights the limitations of existing simulation platforms.

  • Existing Work Existing closed-loop simulation platforms, while useful for validating (M)LLM's application potential, fail to display the human-like understanding and reasoning abilities inherent to (M)LLMs.

Core Contributions

  • Introduction of LimSim++ platform

    • Challenge 1: The absence of long-term closed-loop simulations Current simulation platforms do not provide support for long-term closed-loop simulations, which are crucial for enabling continuous learning and improving generalization in autonomous driving models or systems. LimSim++ addresses this challenge by offering extended-duration, multi-scenario simulations that provide critical information for (M)LLM-driven vehicles.

    • Challenge 2: Ineffective validation of (M)LLM's performance in autonomous driving Current benchmarks for perception, prediction, and planning do not adequately validate the performance of (M)LLMs in autonomous driving. LimSim++ contributes to this matter by supporting research on scenario understanding, decision-making, and evaluation systems. The platform not only introduces a baseline (M)LLM-driven closed-loop framework but also validates it through quantitative experiments across various scenarios, demonstrating the application potential of (M)LLM-driven vehicles.

Implementation and Deployment

The LimSim++ platform achieves integration with (M)LLMs and supports various functions related to autonomous driving, including multimodal prompt generation, decision-making, dynamic evaluation, reflection, and memory. The platform was evaluated through quantitative experiments in different scenarios such as intersections, roundabouts, and ramps, demonstrating the application potential of (M)LLM-driven vehicles.

Summary

LimSim++ is the first closed-loop evaluation platform specifically developed for (M)LLM-driven autonomous driving. It overcomes the limitations of current simulation platforms and validates its effectiveness in various complex traffic scenarios through experimentation.