用大模型当导航助手，智能调节自动驾驶的能耗与可靠性。
MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science
- 让大模型读地图，预测运行参数以平衡能耗与可靠性。
- 导航精度提升20%，计算单元能耗降低11%，机械与计算能耗最高降54%。
- 适合关注自动驾驶能效优化的研究者与工程师。
随着自动驾驶汽车日益普及，高精度、高效系统对提升安全性、性能和能耗管理愈发关键。有效管理能源-可靠性权衡需精准预测车辆运行中的多种工况。近年来，大型语言模型（LLMs）如ChatGPT的显著进步，为自动驾驶相关预测提供了新机遇。本文提出MAPS方法，利用LLMs作为地图阅读协驾者，预测自动驾驶运行中关键参数设置，以实现能源与可靠性的动态平衡。实验表明，MAPS在导航精度上相比最优基线方法提升20%；在计算单元上实现11%的能耗降低，机械与计算单元总能耗最高可减少54%。
原文摘要 · Abstract (English)
As autonomous vehicles become more prevalent, highly accurate and efficient systems are increasingly critical to improve safety, performance, and energy consumption. Efficient management of energy-reliability tradeoffs in these systems demands the ability to predict various conditions during vehicle operations. With the promising improvement of Large Language Models (LLMs) and the emergence of well-known models like ChatGPT, unique opportunities for autonomous vehicle-related predictions have been provided in recent years. This paper proposed MAPS using LLMs as map reader co-drivers to predict the vital parameters to set during the autonomous vehicle operation to balance the energy-reliability tradeoff. The MAPS method demonstrates a 20% improvement in navigation accuracy compared to the best baseline method. MAPS also shows 11% energy savings in computational units and up to 54% in both mechanical and computational units.
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