Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model - Qin, Can; Liang, Bo; Niu, Jia'an; Xiao, Jinghang; Zhu, Shuangkai; Long, Haonan (Array (New York), 2025)
Technical limitations of test equipment, changes in test environment, and jerks of test vehicles under the lighting environment of highway tunnels can lead to the appearance of abnormal psychophysiological data, which affects the data quality and the subsequent prediction and analysis. In this study, the physical quantities of lighting environment and the psychophysiological quantities (heart rate, pupil area, recognition distance and reaction time) of drivers were collected, and the information representation of physical quantities affecting the perception ability of psychophysiological quantities were evaluated and screened in terms of importance of variables by correlation analysis method and LASSO-CV regression method. Based on the screened key physical quantities, particle swarm optimization (PSO) was employed to set the hyper-parameters for stacked denoising autoencoder (SDAE), and the prediction results of the PSO-SDAE model were compared with other network methods, and then the partial dependence plot was used to further explore the intrinsic mechanism of information representation for physical quantities. The results show that the proposed PSO-SDAE model can effectively achieves the reasonable configuration of the SDAE network parameters, and clean abnormal data by mining the hidden information and structural features of normal data. The PSO-SDAE model has an excellent prediction accuracy, stability and cleaning effect when facing different scales and types of normal or abnormal data for psychophysiological quantities. [Display omitted] •PSO algorithm can be used to determine the hyper-parameters in the SDAE network.•PSO-SDAE model can effectively clean and learn the abnormal data.•Correlation between physical and psychophysiological quantities was investigated.