Quadratic approximation based heuristic for optimization-based coordination of automated vehicles in confined areas
Abstract
We investigate the problem of coordinating multiple automated vehicles (AVs) in confined areas. This problem can be formulated as an optimal control problem (OCP) where the motion of the AVs is optimized such that collisions are avoided in cross-intersections, merge crossings, and narrow roads. The problem is combinatorial and solving it to optimality is prohibitively difficult for all but trivial instances. For this reason, we propose a heuristic method to obtain approximate solutions. The heuristic comprises two stages: In the first stage, a Mixed Integer Quadratic Program (MIQP), similar in construction to the Quadratic Programming (QP) sub-problems in Sequential Quadratic Programming (SQP), is solved for the combinatorial part of the solution. In the second stage, the combinatorial part of the solution is held fixed, and the optimal state and control trajectories for the vehicles are obtained by solving a Nonlinear Program (NLP). The performance of the algorithm is demonstrated by a simulation of a non-trivial problem instance.
Keywords
Cite
@article{arxiv.2210.14911,
title = {Quadratic approximation based heuristic for optimization-based coordination of automated vehicles in confined areas},
author = {Stefan Kojchev and Robert Hult and Jonas Fredriksson},
journal= {arXiv preprint arXiv:2210.14911},
year = {2022}
}
Comments
To be published in the 61st IEEE Conference on Decision and Control (CDC 2022). arXiv admin note: substantial text overlap with arXiv:2210.14738