Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements
Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-08-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00568/full |
| _version_ | 1818279038611881984 |
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| author | Ali Darzi Sherif M. Gaweesh Mohamed M. Ahmed Domen Novak |
| author_facet | Ali Darzi Sherif M. Gaweesh Mohamed M. Ahmed Domen Novak |
| author_sort | Ali Darzi |
| collection | DOAJ |
| description | Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems. |
| first_indexed | 2024-12-12T23:26:59Z |
| format | Article |
| id | doaj.art-01da87cebbb14b1581ea7b27b11da54f |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-12T23:26:59Z |
| publishDate | 2018-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-01da87cebbb14b1581ea7b27b11da54f2022-12-22T00:07:59ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-08-011210.3389/fnins.2018.00568392979Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological MeasurementsAli Darzi0Sherif M. Gaweesh1Mohamed M. Ahmed2Domen Novak3Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United StatesDepartment of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United StatesDepartment of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United StatesDepartment of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United StatesDrivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.https://www.frontiersin.org/article/10.3389/fnins.2018.00568/fullhazardous driver statedriving performancephysiological measurementshuman factorsaffective computing |
| spellingShingle | Ali Darzi Sherif M. Gaweesh Mohamed M. Ahmed Domen Novak Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements Frontiers in Neuroscience hazardous driver state driving performance physiological measurements human factors affective computing |
| title | Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements |
| title_full | Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements |
| title_fullStr | Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements |
| title_full_unstemmed | Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements |
| title_short | Identifying the Causes of Drivers’ Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements |
| title_sort | identifying the causes of drivers hazardous states using driver characteristics vehicle kinematics and physiological measurements |
| topic | hazardous driver state driving performance physiological measurements human factors affective computing |
| url | https://www.frontiersin.org/article/10.3389/fnins.2018.00568/full |
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