Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

Qiang Hua,Xianyi Xie,Lisheng Jin,Baicang Guo,Yuying Jiang,Xinqiang Chen

doi:10.1155/2021/6739071

Abstract

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

Highlights

Introduction e World HealthOrganization claims that traffic accidents are the ninth leading cause of death, accounting for 2.2% of all deaths [1]
Pupil Diameter. e variation in the driver's pupil diameter during the driving tasks in the two traffic environments is shown in Figures 5(a) and 5(b). e results demonstrate that, in the traditional and mixed traffic environments, the pupil diameter of the driver increases with the increases of driving load. ese results are consistent with previously reported results [37]
Sixty participants were involved in driving tasks in traditional and mixed traffic environments. e following driver physiological measures, driving performance parameters, and subjective performance were considered: pupil diameter, fixation angle, blink frequency, speed, mean acceleration, standard deviation (SD) of acceleration, SD of the lane positioning (SDLP), mean steering wheel angle, SD of the steering wheel angle (SDSWA), steering entropy (SE), workload assessment performance, and environmental perception performance. e main independent variables were three different driving tasks, and the two traffic environments were traditional and mixed traffic environments

Summary

Introduction

Introduction e World HealthOrganization claims that traffic accidents are the ninth leading cause of death, accounting for 2.2% of all deaths [1]. Evidence indicates that driver distraction is a major cause of road traffic accidents [3, 4]. Many previous studies illustrated that vehicle-based lateral performance parameters, such as Journal of Advanced Transportation the standard deviation (SD) of lane positioning (SDLP), which has been used to evaluate the lane-keeping ability during secondary task driving, can be used to detect distracted driving. Several previous studies focused on steering reversal rates during distracted driving due to phone use and highlighted that a steering reversal rate of 10 represents a distracted driver state; this parameter can be considered an input parameter for detecting cognitive and visual distraction [22]. Many studies have reported that distracted drivers (visual, cognitive) compensate for their driving impairment by reducing their speed [23, 24]. Many studies have reported that distracted drivers (visual, cognitive) compensate for their driving impairment by reducing their speed [23, 24]. e driving speed is affected by the complexity of the driving environment, and the results indicate that the workload of drivers increases as the driving environment becomes more complex; a greater speed reduction is a compensation measure [25, 26]

Methods

Results

Conclusion