The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process.
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http://dx.doi.org/10.1016/j.aap.2021.106156 | DOI Listing |
Traffic Inj Prev
January 2025
National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, China.
Objective: Attention forms the foundation for the formation of situation awareness. Low situation awareness can lead to driving performance decline, which can be dangerous in driving. The goal of this study is to investigate how different types of pre-takeover tasks, involving cognitive, visual and physical resources engagement, as well as individual attentional function, affect driver's attention restoration in conditionally automated driving.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Toronto, Toronto, ON, Canada
Background: Driving cessation among people with cognitive impairments (e.g., Mild Cognitive Impairment; MCI) significantly impacts their independence and overall well‐being.
View Article and Find Full Text PDFProc Hum Factors Ergon Soc Annu Meet
September 2024
University of Waterloo, ON, Canada.
The transition period from automation to manual, known as the takeover process, presents challenges for drivers due to the deficiency in collecting requisite contextual information. The current study collected drivers' eye movement in a simulated takeover experiment, and their Situation Awareness (SA) was assessed using the Situation Awareness Global Assessment Technique (SAGAT) method. The drivers' Stationary Gaze Entropy (SGE) was calculated based on the percentages of time they spent on six pre-defined Areas of Interests (AOIs).
View Article and Find Full Text PDFTraffic Inj Prev
November 2024
Institute of Psychological Science, Hangzhou Normal University, Hangzhou, P. R. China.
Objective: The present study aimed to explore the effects of various tactile takeover requests (TORs) (i.e., tactile sliding TOR and traditional vibration TOR) on the takeover performance in an automated driving system.
View Article and Find Full Text PDFAppl Ergon
February 2025
Department of Industrial Engineering, Tsinghua University, China. Electronic address:
In this study, a conditional automated driving scenario is simulated using virtual reality (VR) technology to explore whether office works presented through augmented reality (AR) affect task and takeover performance, and the neural mechanism was revealed. Sixty-four participants were recruited and their electroencephalography (EEG) was used to measure the brain activities. The results indicated that non-driving-related tasks (NDRTs) requiring higher internal attention focus resulted in poorer task and takeover performance.
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