Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model
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...
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Elsevier
2025-09-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259000562500102X |
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| author | Can Qin Bo Liang Jia'an Niu Jinghang Xiao Shuangkai Zhu Haonan Long |
| author_facet | Can Qin Bo Liang Jia'an Niu Jinghang Xiao Shuangkai Zhu Haonan Long |
| author_sort | Can Qin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-42d2b7245a534844bd1edcf6bbf34507 |
| institution | DOAJ |
| issn | 2590-0056 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| spelling | doaj-art-42d2b7245a534844bd1edcf6bbf345072025-08-20T02:47:13ZengElsevierArray2590-00562025-09-012710047510.1016/j.array.2025.100475Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder modelCan Qin0Bo Liang1Jia'an Niu2Jinghang Xiao3Shuangkai Zhu4Haonan Long5College of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaCollege of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Corresponding author. College of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China.College of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaCollege of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaCollege of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaCollege of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaTechnical 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.http://www.sciencedirect.com/science/article/pii/S259000562500102XLighting environment of road tunnelStacked denoising autoencoderParticle swarm optimizationInformation representationPsychophysiological perception |
| spellingShingle | Can Qin Bo Liang Jia'an Niu Jinghang Xiao Shuangkai Zhu Haonan Long Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model Array Lighting environment of road tunnel Stacked denoising autoencoder Particle swarm optimization Information representation Psychophysiological perception |
| title | Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| title_full | Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| title_fullStr | Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| title_full_unstemmed | Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| title_short | Prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| title_sort | prediction of psychophysiological perception of driver in road tunnel using particle swarm optimization improved stacked denoising autoencoder model |
| topic | Lighting environment of road tunnel Stacked denoising autoencoder Particle swarm optimization Information representation Psychophysiological perception |
| url | http://www.sciencedirect.com/science/article/pii/S259000562500102X |
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