Research On Risk Identification Of Car Following Under Different Distracted Workloads

In the driver-vehicle-road closed-loop road transportation system,the driver is the most unstable link.Driving distraction brings great hidden dangers to traffic safety.Compared with other methods of distraction interference,Distractions are more harmful by mobile phones.Combining the distraction behavior of using mobile phones with common car-following scenarios,establishing a recognition models of collision risk of following vehicles with different workloads during distracted driving can help improve road traffic safety and reduce accidents.Based on the analysis and summary of the existing research,five tasks(We Chat text,We Chat speech,handheld,hands-free,and normal driving)were designed in the paper.Based on the DLab driver’s factor analysis system,conduct simulated driving tests,collect data on visual behavior,vehicle motion,and physiological indicators,study the driving characteristics under different tasks and the differences between the tasks,and finally screen Nine indicators of the entropy of distribution of the fixation point,effective gaze duration,vertical saccade amplitude,blink frequency,standard deviation of vehicle speed,standard deviation of lateral position,standard deviation of lateral acceleration,heart rate growth rate,and RR interval were used as the basic parameters for determining the workload.Principal component analysis and K-means clustering were used to determine the workload of the driver.Based on different workloads,the grid-optimized SVM algorithm was used to establish a driver’s follow-up warning model and verified it.The main research conclusions are as follows:Visually,no matter whether the driver is distracted or not,more than 85% of the visual behavior is gaze.The driver ’s gaze point when following the car is mainly concentrated on the position of the car in front of the field of view,located at the level(0°,10°],vertical(-10°,0°].When performing We Chat text and speech,the driver ’s visual indicators of the entropy of distribution of the fixation point,saccade speed,frequency,amplitude,and duration increase,and the effective gaze duration shortens.The visual distraction time is short in the subtask of We Chat speech,and the indicators change less than We Chat text.When performing hand-held and hands-free tasks,the driver is mainly cognitively distracted,and the saccade behavior indicators are not significantly different.Because the hand-held call involves the driver’s operation distraction makes it harder to concentrate,the information entropy in the gaze area is higher,the effective gaze duration is lower,and the blink frequency is higher.In the vehicle motion state,distraction will cause the driver’s control of the vehicle to deteriorate.We Chat text and voice subtasks that mainly focus on visual distraction will result in a significant increase in the standard deviation of vehicle speed,horizontal position,and lateral acceleration.Handheld and hands-free subtasks with cognitive distraction as the main task have relatively little impact on vehicle control,and the indicators are not significant compared to those without subtasks.Physiologically,due to a certain hysteresis in heart rate,the changes in various indicators during distracted driving are not significant.Based on physiological measurements,the driver’s vision,vehicle motion status,and physiological indicators can be used to determine the workload of distracted driving.Combined with the driver’s response time verification,the results are consistent.Aiming at the different workloads of drivers,the grid-optimized SVM algorithm is adopted,which has a good prediction result for the risk status of following car,with an accuracy rate of 90.5%.Research results at different time points show that the prediction effect 1 second before the accident occurs is the best,with an accuracy rate of 91.3%,a true rate of 92.3%,and a true negative rate of 90.9%.