The Effects of Dynamic Complexity on Drivers' Secondary Task Scanning Behavior under a Car-Following Scenario

• Published in: International journal of environmental research and public health Vol. 19; no. 3; p. 1881
• Main Authors: Wang, Linhong, Li, Hongtao, Guo, Mengzhu, Chen, Yixin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.02.2022; MDPI
• Subjects: Accidents; Accidents, Traffic - psychology; Attention; Automobile Driving - psychology; Automobiles; Behavior; Car following; Carrying capacity; Cognitive psychology; Criteria; Driving; Driving ability; Eye movements; Icons; Interfaces; Physiology; Scanning; Task complexity; Traffic accidents & safety; Traffic capacity; Traffic speed; Vehicle spacing; Vehicles; Velocity; secondary task carrying capacity; dynamic complexity; attention distribution; car-following scenario; traffic safety; automotive engineering
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph19031881

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.