Designing autonomous driving systems requires efficient exploration of large hardware/software configuration spaces under diverse environmental conditions, e.g., with varying traffic, weather, and road layouts. Traditional design space exploration (DSE) approaches struggle with multi-modal execution outputs and complex performance trade-offs, and often require human involvement to assess correctness based on execution outputs. This paper presents a multi-agent, large language model (LLM)-based DSE framework, which integrates multi-modal reasoning with 3D simulation and profiling tools to automate the interpretation of execution outputs and guide the exploration of system designs. Specialized LLM agents are leveraged to handle user input interpretation, design point generation, execution orchestration, and analysis of both visual and textual execution outputs, which enables identification of potential bottlenecks without human intervention. A prototype implementation is developed and evaluated on a robotaxi case study (an SAE Level 4 autonomous driving application). Compared with a genetic algorithm baseline, the proposed framework identifies more Pareto-optimal, cost-efficient solutions with reduced navigation time under the same exploration budget. Experimental results also demonstrate the efficiency of the adoption of the LLM-based approach for DSE. We believe that this framework paves the way to the design automation of autonomous driving systems.

翻译：设计自动驾驶系统需要在多样化的环境条件下（例如变化的交通、天气和道路布局）高效探索庞大的硬件/软件配置空间。传统的设计空间探索方法在处理多模态执行输出和复杂的性能权衡时面临困难，并且通常需要人工介入以基于执行输出评估正确性。本文提出了一种基于多智能体大语言模型的设计空间探索框架，该框架将多模态推理与三维仿真及性能分析工具相结合，以自动化解释执行输出并指导系统设计的探索。通过利用专门的大语言模型智能体来处理用户输入解释、设计点生成、执行编排以及视觉与文本执行输出的分析，该框架能够在无需人工干预的情况下识别潜在瓶颈。我们开发了一个原型实现，并在机器人出租车案例研究（一种SAE 4级自动驾驶应用）上进行了评估。与遗传算法基线相比，所提出的框架在相同的探索预算下，识别出更多帕累托最优且成本效益更高的解决方案，同时减少了导航时间。实验结果也证明了采用基于大语言模型的方法进行设计空间探索的效率。我们相信该框架为自动驾驶系统的设计自动化铺平了道路。