Study on Driver Model Parameters Distribution for Fatigue Driving Levels Based on Quantum Genetic Algorithm

Abstract

According to the characteristics that fatigue study cannot reveal fatigue mechanism and nonlinear influence factors of vehicle driving closed-loop system defects, this paper proposes a driver model inversion method for studying the driver's fatigue diagnosis. Furthermore, the new methods is divided into two steps: 1. using the forecast looks neural network model to build the driver vehicle road closed loop model which is adapted to the complex road conditions. Besides, and the model was used to study the car system parameter changes of the closed-loop system in which the driver is in a state of fatigue. 2. By defining specific movement track in the degree of approximation of theoretical data and taking test data as the objective function, we take the driver parameter inverse problem into multiple target optimization problems. Using a method of real-coded chaotic mutation of quantum genetic algorithm (GA) optimization to obtain the global optimal solution. The driving simulation test results show that under the condition of complex road conditions, the proposed algorithm in actual driving parameter inversion of the alignment is superior to the traditional genetic algorithm (GA) and the traditional quantum genetic algorithm (QGA), Finally the pilot model parameters the relationship between fatigue factors are made.