Citation

Ma J, Gong Z, Tan J, Zhang Q, Zuo Y. Transp. Res. F Traffic Psychol. Behav. 2020; 69: 235-250.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trf.2020.01.016

PMID

unavailable

Abstract

With the rapid development of human-machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.

Language: en

Keywords

Data mining; Driving distraction; Eye tracking; Random forest; Secondary task; Vehicle HMI