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According to the assumption 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 method is divided into two steps: 1. By using the forecast of 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 changes in the closed-loop car system parameter in which the driver is in a state of
fatigue. 2. By defining specific movement track through the degree of approximation of theoretical data and taking test
data as the objective function, the driver parameter inverse problem was broken into multiple target optimization problems.
A method of real-coded chaotic mutation of quantum genetic algorithm (GA) optimization is used to find 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 relationship between pilot model parameters and
fatigue factors is established.