Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data
In order to improve the driver’s physiological and psychological state, the driver’s mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. The experimental schemes have been scientifically desig...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/5215479 |
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| _version_ | 1832561325509705728 |
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| author | Weiwei Qi Bin Shen Linhong Wang |
| author_facet | Weiwei Qi Bin Shen Linhong Wang |
| author_sort | Weiwei Qi |
| collection | DOAJ |
| description | In order to improve the driver’s physiological and psychological state, the driver’s mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. The experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver’s spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver’s spatiotemporal data are analyzed to judge the changing trend of the driver’s workload in the tunnel environment. The results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver’s typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. The tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design. |
| format | Article |
| id | doaj-art-e55254e68004439f816ffe456299f607 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-e55254e68004439f816ffe456299f6072025-02-03T01:25:26ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/52154795215479Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal DataWeiwei Qi0Bin Shen1Linhong Wang2School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, ChinaSchool of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, ChinaSchool of Transportation, Jilin University, Changchun, Jilin 130012, ChinaIn order to improve the driver’s physiological and psychological state, the driver’s mental load which is caused by sight distance, lighting, and other factors in the tunnel environment should be quantified via modeling the spatiotemporal data. The experimental schemes have been scientifically designed based on methods of traffic engineering and human factor engineering, which aims to test the driver’s spatiotemporal data of eye movement and ECG (electrocardiogram) index in the tunnel environment. Firstly, the changes in the driver’s spatiotemporal data are analyzed to judge the changing trend of the driver’s workload in the tunnel environment. The results show that the cubic spline interpolation function model can fit the dynamic changes of average pupil diameter and heart rate (HR) growth rate well, and the goodness of fit for the model group is above 0.95. So, tunnel environment makes the driver’s typical physiological indicators fluctuate in the coordinates of time and space, which can be modeled and quantified. Secondly, in order to analyze the classification of tunnel risk level, a fusion model has been built based on the functions of average pupil diameter and HR growth rate. The tunnel environmental risk level has been divided into four levels via the fusion model, which can provide a guidance for the classification of tunnel risk level. Furthermore, the fusion model allows tunnel design and construction personnel to adopt different safety design measures for different risk levels, and this method can effectively improve the economy of tunnel operating safety design.http://dx.doi.org/10.1155/2020/5215479 |
| spellingShingle | Weiwei Qi Bin Shen Linhong Wang Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data Journal of Advanced Transportation |
| title | Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data |
| title_full | Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data |
| title_fullStr | Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data |
| title_full_unstemmed | Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data |
| title_short | Model of Driver’s Eye Movement and ECG Index under Tunnel Environment Based on Spatiotemporal Data |
| title_sort | model of driver s eye movement and ecg index under tunnel environment based on spatiotemporal data |
| url | http://dx.doi.org/10.1155/2020/5215479 |
| work_keys_str_mv | AT weiweiqi modelofdriverseyemovementandecgindexundertunnelenvironmentbasedonspatiotemporaldata AT binshen modelofdriverseyemovementandecgindexundertunnelenvironmentbasedonspatiotemporaldata AT linhongwang modelofdriverseyemovementandecgindexundertunnelenvironmentbasedonspatiotemporaldata |