MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science
<|reference_start|>MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles Through LLMs Penetrated Science: 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.<|reference_end|>
@article{aliazam2024maps:,
    title={MAPS: Energy-Reliability Tradeoff Management in Autonomous Vehicles
  Through LLMs Penetrated Science},
    author={Mahdieh Aliazam, Ali Javadi, Amir Mahdi Hosseini Monazzah, and Ahmad
  Akbari Azirani},
    journal={arXiv preprint arXiv:2409.06558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06558},
    primaryClass={cs.AR cs.RO}
}