用多智能体大模型自动探索自动驾驶系统设计空间，省去人工分析。
A Multi-Agent LLM Framework for Design Space Exploration in Autonomous Driving Systems
- 多智能体分工协作：解析输入、生成方案、调度执行、分析输出。
- 在相同预算下，比遗传算法发现更多低成本高效解，导航时间更短。
- 适合自动驾驶系统设计者，尤其关注自动化与性能优化的团队。
自动驾驶系统的设计需在不同交通、天气和道路布局条件下，高效探索庞大的软硬件配置空间。传统设计空间探索（DSE）方法难以处理多模态执行输出和复杂性能权衡，且常需人工评估执行结果的正确性。本文提出一种基于多智能体大语言模型（LLM）的DSE框架，融合多模态推理、3D仿真与性能分析工具，实现对执行输出的自动化解读，并引导系统设计探索。专用LLM智能体分别负责用户输入理解、设计点生成、执行编排及视觉与文本输出分析，可在无须人工干预的情况下识别潜在瓶颈。在机器人出租车案例（SAE Level 4）上实现了原型系统并进行评估。相比遗传算法基线，该框架在相同探索预算下发现了更多帕累托最优、成本效益更高的解决方案，且导航时间显著降低。实验表明，基于LLM的方法在DSE中具有高效性。我们认为该框架为自动驾驶系统设计自动化开辟了新路径。
原文摘要 · Abstract (English)
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.
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