Real-time decision-making for autonomous vehicles under faults
Abstract
This paper addresses the challenges of decision-making for autonomous vehicles under faults during a transport mission. A real-time decision-making problem of vehicle routing planning considering maintenance management is formulated as an optimization problem. The goal is to minimize the total time to finish the transport mission by selecting the optimal workshop to conduct the maintenance and the corresponding routes. Two methods are proposed to solve the optimization problem based on two methods of fundamental solutions: (1) Mixed Integer Programming; (2) Dijkstra's algorithm. We adapt these methods to solve the optimization problem and consider improving the computation efficiency. Numerical studies of test cases of highway and urban scenarios are presented to demonstrate the proposed methods, which show the feasibility and high computational efficiency of both methods.
Cite
@article{arxiv.2202.04554,
title = {Real-time decision-making for autonomous vehicles under faults},
author = {Xin Tao and Zhao Yuan},
journal= {arXiv preprint arXiv:2202.04554},
year = {2022}
}
Comments
Accepted by the IEEE 9th International Conference on Industrial Engineering and Applications (ICIEA 2022). Date: November 2021. Email: taoxin@kth.se, zhaoyuan@hi.is, zhaoyuan.epslab@gmail.com