Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines

Abstract

As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this strategy, this paper applied support vector machines (SVMs), which is a data mining method, to develop a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data. Data were collected in a simulator experiment in which ten participants interacted with an IVIS while driving. The data were used to train and test both SVM and logistic regression models, and three different model characteristics were investigated: how distraction was defined, which data were input to the model, and how the input data were summarized. The results show that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models. The best performing model (96.1% accuracy) resulted when distraction was defined using experimental conditions (i.e., IVIS drive or baseline drive), the input data were comprised of eye movement and driving measures, and these data were summarized over a 40-s window with 95% overlap of windows. These results demonstrate that eye movements and simple measures of driving performance can be used to detect driver distraction in real time. Potential applications of this paper include the design of adaptive in-vehicle systems and the evaluation of driver distraction