Online Prediction of Driver Distraction Based on Brain Activity Patterns

• Published in: IEEE transactions on intelligent transportation systems Vol. 16; no. 1; pp. 136 - 150
• Main Authors: Shouyi Wang, Yiqi Zhang, Changxu Wu, Darvas, Felix, Chaovalitwongse, Wanpracha Art
• Format: Journal Article
• Language: English
• Published: IEEE 01.02.2015
• Subjects: Driver-distraction prediction; electroencephalographic (EEG) signals; Electroencephalography; Feature extraction; Monitoring; Navigation; online adaptive predictions; Roads; time-series pattern recognition; Urban areas; Vehicles; online adaptive predictions; time-series pattern recognition; Driver-distraction prediction; electroencephalographic (EEG) signals
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2014.2330979

This paper presents a new computational framework for early detection of driver distractions (map viewing) using brain activity measured by electroencephalographic (EEG) signals. Compared with most studies in the literature, which are mainly focused on the classification of distracted and nondistracted periods, this study proposes a new framework to prospectively predict the start and end of a distraction period, defined by map viewing. The proposed prediction algorithm was tested on a data set of continuous EEG signals recorded from 24 subjects. During the EEG recordings, the subjects were asked to drive from an initial position to a destination using a city map in a simulated driving environment. The overall accuracy values for the prediction of the start and the end of map viewing were 81% and 70%, respectively. The experimental results demonstrated that the proposed algorithm can predict the start and end of map viewing with relatively high accuracy and can be generalized to individual subjects. The outcome of this study has a high potential to improve the design of future intelligent navigation systems. Prediction of the start of map viewing can be used to provide route information based on a driver's needs and consequently avoid map-viewing activities. Prediction of the end of map viewing can be used to provide warnings for potential long map-viewing durations. Further development of the proposed framework and its applications in driver-distraction predictions are also discussed.