Citation

Arrigoni S, Braghin F, Cheli F. Veh. Syst. Dyn. 2022; 60(12): 4118-4143.

Copyright

(Copyright © 2022, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2021.1999991

PMID

unavailable

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.

Language: en

Keywords

autonomous driving; GA methods; motion planning; MPC; obstacle avoidance