Driver Cognitive Distraction Detection Using Driving Performance Measures
Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data,...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2012/432634 |
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| _version_ | 1832561694205804544 |
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| author | Lisheng Jin Qingning Niu Haijing Hou Huacai Xian Yali Wang Dongdong Shi |
| author_facet | Lisheng Jin Qingning Niu Haijing Hou Huacai Xian Yali Wang Dongdong Shi |
| author_sort | Lisheng Jin |
| collection | DOAJ |
| description | Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state. |
| format | Article |
| id | doaj-art-29748406b6e947f7890fb71e04c0f801 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-29748406b6e947f7890fb71e04c0f8012025-02-03T01:24:22ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2012-01-01201210.1155/2012/432634432634Driver Cognitive Distraction Detection Using Driving Performance MeasuresLisheng Jin0Qingning Niu1Haijing Hou2Huacai Xian3Yali Wang4Dongdong Shi5Transportation College, Jilin University, Changchun, Jilin 130022, ChinaTransportation College, Jilin University, Changchun, Jilin 130022, ChinaTransportation College, Jilin University, Changchun, Jilin 130022, ChinaTransportation College, Jilin University, Changchun, Jilin 130022, ChinaTransportation College, Jilin University, Changchun, Jilin 130022, ChinaTransportation College, Jilin University, Changchun, Jilin 130022, ChinaDriver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures. Characteristic parameters could be directly extracted from Controller Area Network-(CAN-)Bus data, without depending on other sensors, which improves real-time and robustness performance. Three cognitive distraction states (no cognitive distraction, low cognitive distraction, and high cognitive distraction) were defined using different secondary tasks. NLModel, NHModel, LHModel, and NLHModel were developed using SVMs according to different states. The developed system shows promising results, which can correctly classify the driver’s states in approximately 74%. Although the sensitivity for these models is low, it is acceptable because in this situation the driver could control the car sufficiently. Thus, driving performance measures could be used alone to detect driver cognitive state.http://dx.doi.org/10.1155/2012/432634 |
| spellingShingle | Lisheng Jin Qingning Niu Haijing Hou Huacai Xian Yali Wang Dongdong Shi Driver Cognitive Distraction Detection Using Driving Performance Measures Discrete Dynamics in Nature and Society |
| title | Driver Cognitive Distraction Detection Using Driving Performance Measures |
| title_full | Driver Cognitive Distraction Detection Using Driving Performance Measures |
| title_fullStr | Driver Cognitive Distraction Detection Using Driving Performance Measures |
| title_full_unstemmed | Driver Cognitive Distraction Detection Using Driving Performance Measures |
| title_short | Driver Cognitive Distraction Detection Using Driving Performance Measures |
| title_sort | driver cognitive distraction detection using driving performance measures |
| url | http://dx.doi.org/10.1155/2012/432634 |
| work_keys_str_mv | AT lishengjin drivercognitivedistractiondetectionusingdrivingperformancemeasures AT qingningniu drivercognitivedistractiondetectionusingdrivingperformancemeasures AT haijinghou drivercognitivedistractiondetectionusingdrivingperformancemeasures AT huacaixian drivercognitivedistractiondetectionusingdrivingperformancemeasures AT yaliwang drivercognitivedistractiondetectionusingdrivingperformancemeasures AT dongdongshi drivercognitivedistractiondetectionusingdrivingperformancemeasures |