MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science

TL;DR

This paper introduces MAPS, a novel approach using Large Language Models as co-drivers to predict key parameters for balancing energy efficiency and reliability in autonomous vehicles, achieving significant improvements.

Contribution

The paper presents MAPS, the first method leveraging LLMs for energy-reliability tradeoff management in autonomous vehicles, demonstrating notable accuracy and energy efficiency gains.

Findings

20% improvement in navigation accuracy

11% energy savings in computational units

54% savings in mechanical and computational units

Abstract

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.