Cognitive Distraction State Recognition of Drivers at a Nonsignalized Intersection in a Mixed Traffic Environment

Qiang Hua,Ming Gao,Lisheng Jin,Yuying Jiang,Baicang Guo,Songtao Lv

doi:10.1155/2021/6676807

Abstract

Distracted driving has become a growing traffic safety concern. With advances in autonomous driving and connected vehicle technology, a mixture of various types of intelligent vehicles will become normal in the near future, while more factors that may cause driver cognitive distraction are emerging. However, there are rarely studies on distracted driving in mixed traffic environments. To fill this gap, we conducted a natural driving experiment with three representative events at a nonsignalized intersection in a mixed traffic environment and proposed a novel method of identifying cognitive distraction based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism. Forty participants were recruited for each event, who completed three different cognitive distraction experiments induced by three different secondary tasks in contrast with a normal driving process when passing a nonsignalized intersection. Related driving performance and eye movement data were collected to train and test the Bi-LSTM with attention mechanism model. Compared with the support vector machine (SVM) model, its recognition accuracy rate is 94.33%, which is 3.83% higher than that of the SVM in the total event, which has reasonable applicability for distraction recognition in a mixed traffic environment. Potential applications of this model include distraction alarm and autonomous driving assistance systems, which could avoid road traffic accidents.

Highlights

Distracted driving has become a dominant cause of traffic accidents [1]
Compared to the support vector machine (SVM) model, the results show that the BiLSTM with attention mechanism model can reliably recognize the distracted driving behaviors of the participants with the best accuracy rate of 94.33% in the total event, which is 3.83% higher than that of SVM in the total event, and it is suitable for distraction recognition in a mixed traffic environment. e findings of this study can be applied to distracted driving alarms for autonomous driving assistance systems
To explore a driver cognitive distraction recognition method in a mixed traffic environment, this paper presents a method for identifying cognitive distraction based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism at a nonsignalized intersection in a mixed traffic environment, using the combinations of the optimal feature indices as inputs to the classifier

Summary

Introduction

Distracted driving has become a dominant cause of traffic accidents [1]. In the United States, 3450 people died of distracted driving in 2016 [2]. E researchers [22] proposed a real-time method for detecting a driver’s cognitive and visual distraction using lateral driving performance measures, and radial basis probabilistic neural networks (RBPNNs) were adopted to construct classification models. All of the above studies were based on traditional traffic scenarios and did not involve mixed traffic scenarios To fill this gap, this study proposes a method based on bidirectional long short-term memory (Bi-LSTM) with attention mechanism [23] to identify driver cognitive distraction at a nonsignalized intersection in a mixed traffic environment [24]. Multiple real vehicle experiments are performed to collect driving performance data for distracted driving at nonsignalized intersections in mixed traffic environments, such as vehicle longitudinal and lateral control data as well as eye movement data from an eye tracker; the driver’s physiological state information is collected by a BIOPAC physiological recorder. Compared to the SVM model, the results show that the BiLSTM with attention mechanism model can reliably recognize the distracted driving behaviors of the participants with the best accuracy rate of 94.33% in the total event, which is 3.83% higher than that of SVM in the total event, and it is suitable for distraction recognition in a mixed traffic environment. e findings of this study can be applied to distracted driving alarms for autonomous driving assistance systems

Experimental System and Methods

Feature Screening and Model Construction

13 Event Total event

Discussion

Limitations of this study are as follows: