Model-Based Analysis and Classification of Driver Distraction Under Secondary Tasks

• Published in: IEEE transactions on intelligent transportation systems 2010-09, Vol.11 (3), p.692-701
• Main Authors: Ersal, T, Fuller, H J A, Tsimhoni, O, Stein, J L, Fathy, H K
• Format: Article
• Language: English
• Subjects: Applied sciences; Automotive engineering; Classification; Cognition & reasoning; Computer science; control theory; systems; Control systems; Data processing. List processing. Character string processing; Driver distraction; driver modeling; Drivers; Driving; Driving conditions; Exact sciences and technology; Ground, air and sea transportation, marine construction; Mathematical models; Memory organisation. Data processing; Neural networks; Predictive models; Radio control; Road transportation and traffic; Safety; secondary task; Software; support vector machine (SVM); Support vector machine classification; Support vector machines; Tasks; Vehicle crash testing; Vehicle driving
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2010.2049741

It is well established in the literature that secondary tasks adversely affect driving behavior. Previous research has focused on discovering the general trends by analyzing the average effects of secondary tasks on a population of drivers. This paper conjectures that there may also be individual effects, i.e., different effects of secondary tasks on individual drivers, which may be obscured within the average behavior of the population, and proposes a model-based approach to analyze them. Specifically, a radial-basis neural-network-based modeling framework is developed to characterize the normal driving behavior of a driver when driving without secondary tasks. The model is then used in a scenario of driving with a secondary task to predict the hypothetical actions of the driver, had there been no secondary tasks. The difference between the predicted normal behavior and the actual distracted behavior gives individual insight into how the secondary tasks affect the driver. It is shown that this framework can help uncover the different effects of secondary tasks on each driver, and when used together with support vector machines, it can help systematically classify normal and distracted driving conditions for each driver.