Behavior signal processing for vehicle applications

Within the past decade, analyzing and modeling human behavior by processing large amounts of collected data has become an active research field in the area of human–machine interaction. The research community is striving to find principled ways to explain and represent important behavioral characteristics of humans, with the goal of developing more efficient and more effective cooperative interactions between humans, machines, and environment. This paper provides a summary of the progress we have achieved to date in our study, which has focused specifically on interactions between driver, vehicle, and driving environment. First, we describe the method of data collection used to develop our on-the-road driving data corpus. We then provide an overview of the data-driven, signal processing approaches we used to analyze and model driver behavior for a wide range of practical vehicle applications. Next, we perform experimental validation by observing the actual driving behavior of groups of real drivers. In particular, the vehicle applications of our research include driver identification, behavior prediction related to car following and lane changing, detection of emotional frustration, and improving driving safety through driver coaching. We hope this paper will provide some insight to researchers with an interest in this field, and help identify areas and applications where further research is needed.