The ability of eye-tracking metrics to classify and predict the perceived driving workload |
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| Affiliation: | 1. Department of Industrial and Management Engineering, Incheon National University (INU), Academy-ro 119, Yeonsu-gu, Incheon, 22012, Republic of Korea;2. School of Information Convergence, Kwangwoon University, Kwangwoon-ro 20, Nowon-gu, Seoul, 01897, Republic of Korea;1. College of Robotics, Beijing Union University, Beijing, 100027, China;2. Ergonomics Laboratory, China National Institute of Standardization, Beijing, 100191, China;3. SAMR Key Laboratory of Human Factors and Ergonomics, Beijing, 100820, China;4. School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China;5. School of Graduate Studies and Research, Meharry Medical College, Nashville, TN, USA;1. International Institut of Biomechanics and Surgical Ergonomics , Université de Toulon, CS60584-83041, TOULON CEDEX 9, France;2. Laboratoire HandiBio, EA 4322, Université de Toulon, CS60584-83041, TOULON CEDEX 9, France;1. Sensory and Motor Control, Graduate School of Medical Sciences, Kitasato University, Kitasato 1-15-1, Minami, Sagamihara, Kanagawa, 252-0373, Japan;2. Physical Therapy Course, Department of Rehabilitation, School of Allied Health Sciences, Kitasato University, Kitasato 1-15-1, Minami, Sagamihara, Kanagawa, 252-0373, Japan;3. Development Department, Kotobuki Seating Co. Ltd., Inadaira 1-70-2, Musashimurayama, Tokyo, 208-8555, Japan;1. School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China;2. Department of Computer Graphics Technology, Purdue University, West Lafayette, IN, United States;1. College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, 150001, China;2. Management Department, East University of Heilongjiang, Harbin, 150086, China;3. Wood Industry College, Vietnam National University of Forestry, Hanoi, 10000, Viet Nam |
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| Abstract: | Traffic safety is directly related to the mental and physical condition of the driver. Performing regular secondary tasks while driving is an additional activity that dissipates attention and adds to the drivers' workload. Identifying driver fatigue and workload based on gaze behavior is one way to ensure a safe driving experience. The purpose of this paper is to classify and predict driving perceived workload using a set of eye-tracking metrics (gaze fixation, duration, pointing, and pupil diameter). The ability of eye-tracking metrics to predict driving workload has been investigated. As a result, frustration, performance, and temporal load showed a correlation with gaze metrics. Gaze point, duration, fixation, and pupil diameter significantly influence driving workload.Relevance to industry: Results will supply the specialists in eye-tracking/sensor technologies and traffic safety with new knowledge to improve the design of the driving performance and safety monitoring systems and efficiency of the driving process. |
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| Keywords: | Eye-tracking Driving workload Gaze duration Gaze fixation Regression analysis |
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