Darzi Ali, Gaweesh Sherif M, Ahmed Mohamed M, Novak Domen
Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United States.
Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United States.
Front Neurosci. 2018 Aug 14;12:568. doi: 10.3389/fnins.2018.00568. eCollection 2018.
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.
驾驶员危险的身心状态（如注意力分散、疲劳、压力和高工作负荷）对驾驶表现有重大影响，在所有交通事故中占比25%至50%。这些状态由多种因素引起，如使用手机或睡眠不足。然而，虽然在检测危险状态方面已开展大量研究，但大多数研究并未尝试识别危险状态的成因。此类信息将非常有用，因为它能让智能车辆更好地应对检测到的危险状态。因此，本研究探讨了能否通过结合驾驶员特征、车辆运动学和生理指标自动识别驾驶员危险状态的成因。21名健康参与者参加了四次时长45分钟的模拟驾驶实验，其中两次实验中他们处于轻度睡眠不足状态。在每次实验中，有八种不同场景，包括不同天气（晴天或下雪天）、交通密度以及手机使用情况（使用或不使用手机）。在每个场景中，监测了四项生理指标（呼吸、心电图、皮肤电导和体温）以及八项车辆运动学指标。此外，还获取了三项自我报告的驾驶员特征：性格、压力水平和情绪。基于驾驶员特征、车辆运动学和生理信号形成了三个特征集。这三个特征集的所有可能组合被用于对睡眠不足（困倦与警觉）、交通密度（低与高）、手机使用情况以及天气状况（雾天/雪天与晴天）进行分类，准确率分别高达98.8%、91.4%、82.3%和71.5%。车辆运动学指标在天气和交通密度分类中最为有用，而生理指标和驾驶员特征在睡眠不足和手机使用情况分类中较为有用。此外，还测试了另一种分类方案，该方案还纳入了是否存在其他危险状态成因的信息，不过这并未带来更高的分类准确率。未来，这些分类器可用于识别驾驶员危险状态的存在及其成因，这可为更智能的干预系统奠定基础。
