Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network
The driver is one of the most important factors in road traffic. Monitoring the driver’s driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers’ safety on the road, particula...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/6897781 |
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| _version_ | 1832564750286848000 |
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| author | Hao Han Kejie Li Yi Li |
| author_facet | Hao Han Kejie Li Yi Li |
| author_sort | Hao Han |
| collection | DOAJ |
| description | The driver is one of the most important factors in road traffic. Monitoring the driver’s driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers’ safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in different monotonous driving scenarios. A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was significantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach. |
| format | Article |
| id | doaj-art-5871f79d36194a6bb038dac4e07651c0 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5871f79d36194a6bb038dac4e07651c02025-02-03T01:10:19ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/6897781Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory NetworkHao Han0Kejie Li1Yi Li2Logistics Engineering CollegeInstitute of Logistics Science and EngineeringInstitute of Logistics Science and EngineeringThe driver is one of the most important factors in road traffic. Monitoring the driver’s driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers’ safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in different monotonous driving scenarios. A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was significantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach.http://dx.doi.org/10.1155/2022/6897781 |
| spellingShingle | Hao Han Kejie Li Yi Li Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network Journal of Advanced Transportation |
| title | Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network |
| title_full | Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network |
| title_fullStr | Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network |
| title_full_unstemmed | Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network |
| title_short | Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network |
| title_sort | monitoring driving in a monotonous environment classification and recognition of driving fatigue based on long short term memory network |
| url | http://dx.doi.org/10.1155/2022/6897781 |
| work_keys_str_mv | AT haohan monitoringdrivinginamonotonousenvironmentclassificationandrecognitionofdrivingfatiguebasedonlongshorttermmemorynetwork AT kejieli monitoringdrivinginamonotonousenvironmentclassificationandrecognitionofdrivingfatiguebasedonlongshorttermmemorynetwork AT yili monitoringdrivinginamonotonousenvironmentclassificationandrecognitionofdrivingfatiguebasedonlongshorttermmemorynetwork |