MAPS：通过LLMs渗透科学实现自动驾驶车辆中能量-可靠性权衡管理
MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science
AI总结：
本文提出MAPS方法，利用LLMs作为地图读取协同驾驶员预测自动驾驶关键参数以平衡能量-可靠性权衡，导航精度较最佳基线提升20%，计算单元节能11%，综合节能最高54%。
摘要：
随着自动驾驶车辆日益普及，高精度且高效的系统对于提升安全性、性能和能耗表现愈发关键。在这些系统中高效管理能量-可靠性权衡，需要具备在车辆运行期间预测各种工况的能力。近年来，随着大型语言模型（LLMs）的显著进步以及ChatGPT等知名模型的出现，为自动驾驶车辆相关预测提供了独特机遇。本文提出了MAPS方法，利用LLMs作为地图读取协同驾驶员，预测自动驾驶车辆运行期间需要设定的关键参数，以平衡能量-可靠性权衡。MAPS方法在导航精度上相比最佳基线方法提升了20%。MAPS在计算单元上还实现了11%的能耗节省，在机械单元和计算单元合计上最高节省达54%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
