@article{ref1,
title="MPC trajectory planner for autonomous driving solved by genetic algorithm technique",
journal="Vehicle system dynamics",
year="2022",
author="Arrigoni, S. and Braghin, F. and Cheli, F.",
volume="60",
number="12",
pages="4118-4143",
abstract="Focusing on autonomous driving algorithm development, this paper proposes a novel real-time trajectory planner formulated as a Nonlinear Model Predictive Control (NMPC) algorithm. The mathematical formulation of the problem is deeply reported and discussed. The numerical solution of the NMPC problem is the result of a novel genetic algorithm strategy that represents the innovative aspect of the work proposed. The aim of this paper is also to show how genetic algorithm can be a valid approach for motion planning strategies. Numerical results are discussed through simulations that show a reasonable behaviour of the proposed strategy in the presence of moving obstacles as well as in a wide range of road friction conditions. Moreover, a real-time implementation for research purposes is assumed as possible by considering computational time analysis reported.<p /> <p>Language: en</p>",
language="en",
issn="0042-3114",
doi="10.1080/00423114.2021.1999991",
url="http://dx.doi.org/10.1080/00423114.2021.1999991"
}