Case-based prediction of teen driver behavior and skill

Abstract

Motor vehicle crashes are the leading cause of death for U.S. teens, accounting for more than one in three deaths in this age group and claiming the lives of about eight teenagers a day, according to the 2010 report by the Center for Disease Control and Prevention1. In order to inform new training methods and new technology to accelerate learning and reduce teen crash risk, a more complete understanding of this complex driving behavior was needed. In this application paper we present our first step towards deploying case-based techniques to model teenage driver behavior and skill level. Specifically, we present our results in using case-based reasoning (CBR) to model both the vehicle control behavior and the skill proficiency of teen drivers by using data collected in a high-fidelity driving simulator. In particular, we present a new similarity measure to compare behavioral data based on feature selection methods, which achieved good results in predicting behavior and skill.