MAPS：LLMを活用した自動運転車のエネルギー・信頼性トレードオフ管理
MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science
地図読み取りの共同ドライバーとしてLLMを用い、自動運転車の運転中にエネルギーと信頼性のバランスを取るパラメータを予測する手法を提案し、ナビ精度と省エネを改善した。
著者: Mahdieh Aliazam, Ali Javadi, Amir Mahdi Hosseini Monazzah, Ahmad Akbari Azirani
分類: cs.AR, cs.RO
原文アブストラクト
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.