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

arXiv: https://arxiv.org/abs/2409.06558 · HTML: https://mlsanity.com/paper/2409.06558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Placed in

- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 7 of 7)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2311.12351/) (2023)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception](https://mlsanity.com/paper/2202.11330/) (2022)
- [BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems](https://mlsanity.com/paper/2307.10041/) (2023)
- [A Superalignment Framework in Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2406.05651/) (2024)
- [Energy-Efficient Mobile Robot Control via Run-time Monitoring of Environmental Complexity and Computing Workload](https://mlsanity.com/paper/2109.04285/) (2021)

## Abstract (arXiv)

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.