Abstract
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.
Highlights
The relationship between the average single scanning time and the vehicle speed can be expressed by a negative logarithmic regression model (R2 = 0.962), the relationship between the average single scanning time and the vehicle spacing can be expressed by a positive logarithmic regression model (R2 = 0.992), and the relationship between the average single scanning time and the number of icons can be expressed by a positive linear regression model (R2 = 0.735)
To reduce traffic accidents caused by driving distraction, we studied the impact of dynamic complexity on secondary tasks carrying capacity under different traffic scenarios
The relationship between vehicle speed, vehicle spacing, the number of icons, and average single scanning time can be expressed by a negative logarithmic model, a positive logarithmic model, and a positive linear model, respectively
Summary
Automobile companies are equipped with more and more electronic equipment in cars, to meet the demands of various consumers. With the improvement of the network and electronic degree of vehicles, driver’s demands for multitasking operation of the entertainment system, real-time onboard information system, and smartphones in the car are significantly increased [2]. The user interface of automotive interaction systems has become more complex, which makes these intelligent devices important causes of drivers’ distraction and, important factors that contribute to accidents [3]. With the development of sensor technology, communication technology, and the continuous proposal of the concepts of intelligence and networking, data openness and information sharing between vehicles will become an inevitable trend [4], providing a new possibility for the development of adaptive vehicle human–computer interaction systems
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