Support vector machines for the identification of real-time driving distraction using in-vehicle information systems

Abstract

IVIS (In-vehicle Information System) is an important factor causing driver distraction. To study the driver distraction detection method when operating IVIS, the effectiveness of driving performance indicators in the identification of driving distraction was verified by the method of variance analysis. Forty participants were selected to conduct the driver distraction experiment when operating IVIS, and the data of driving performance indicators such as eye movement, speed, et al. were obtained. According to the driving performance data of IVIS, a real-time detection of distraction based on driving performance was built by using support vector machine, and three kernel functions of the model were conducted comparative analysis and validation. The results show that SVM models can effectively evaluate the degree of drivers’ distraction. At the same time, when the Radial Basis Function is used as a kernel function, the accuracy for recognizing driver distraction is 89.9%, which is higher than when the sigmoid polynomial kernel function and SAVE-IT model are used. The research could be applied in the design of adaptive in-vehicle systems and the evaluation of driving distraction, providing theoretical support and reference for the development of vehicle-mounted information systems and the management of driver distraction prevention measures.

Highlights

• We verify the effectiveness of driving performance indicators as decision variables for driving distraction.

• We use driving performance indicators as inputs to a distraction detection algorithm.

• We examine changes in the level of information about driving distraction.

• SVMs method can be used to detect driving distraction during IVIS operations in real time.

• The accuracy for using Radial Basis Function to recognizing the driver’s level of distraction is higher than the Radial Basis Function.

Keywords:

• distraction detection
• driver distraction
• driving performance
• in-vehicle information system
• kernel function
• SVM model

Acknowledgments

The authors would like to extend our sincere appreciation to those individuals who so graciously provided access to their vehicles, systems, and devices for purposes of our review.

Additional information

Funding

This research was supported by National Key Research and Development Program of China (2017YFC0803901), Heilongjiang Highway Survey and Design Institute Project (2018006).