Extraction Method of Driver’s Mental Component Based on Empirical Mode Decomposition and Approximate Entropy Statistic Characteristic in Vehicle Running State

Abstract

In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.

Validity of Mental Workload Measures in a Driving Simulation Environment

“…Mental workload is a multifaceted psychological construct subject to different definitions [13]. One of the main approaches to mental workload assessment refers to the multiple resources model [14].…”

Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA

“…Existing driving fatigue detection methods are divided into four categories: detection methods based on subjective factors, detection methods based on vehicle driving characteristics [ 4 , 5 , 6 ], detection methods based on machine vision characteristics [ 7 , 8 , 9 ], and detection methods based on human physiological signals [ 10 , 11 , 12 , 13 ]. Among them, detection methods based on subjective factors are generally used as auxiliary methods for driving fatigue detection.…”

Analysis of Driving Control Characteristics in Typical Road Types

“…In the view of [19,20], a low curve radius was the riskiest deficiency related to road geometry, but lane width, shoulder width, and horizontal geometry were also proven to increase road risk rates [21,22]. Chu et al [23] proposed an improved curve speed model considering driving styles as well as vehicle and road factors. However, Carsten et al hold that it is not certain that the results obtained in one road type are still available in other road-alignment-based research, so that such results lack practical applications [24].…”