Autonomous vehicles usually consume a large amount of computational power for their operations, especially for the tasks of sensing and perception with artificial intelligence algorithms. Such a computation may not only cost a significant amount of energy but also cause performance issues when the onboard computational resources are limited. To address this issue, this paper proposes an adaptive optimization method to online allocate the onboard computational resources of an autonomous vehicle amongst multiple vehicular subsystems depending on the contexts of the situations that the vehicle is facing. Different autonomous driving scenarios were designed to validate the proposed approach and the results showed that it could help improve the overall performance and energy consumption of autonomous vehicles compared to existing computational arrangement.
@article{arxiv.2102.07000,
title = {Adaptive Optimization of Autonomous Vehicle Computational Resources for Performance and Energy Improvement},
author = {Saurabh Jambotkar and Longxiang Guo and Yunyi Jia},
journal= {arXiv preprint arXiv:2102.07000},
year = {2021}
}