In this project we will employ a user-centered design approach to develop an educational and technological intervention program to prevent drowsy driving crashes among shift-workers.
In this project we are developing a model of human behavior during automation failures that may be integrated into current and future design processes for automated vehicles. We will use this model to generate a set of design guidelines for future automated vehicle following technologies that will promote safety and reduce automated driving crashes.
This goal of this project is to use innovative data analysis and reduction techniques to design state of the art algorithms for detecting drowsy driving. The project specifically focuses on integrating driving context (i.e. types of roads, traffic, road condition) and various graphical modeling approaches (e.g. Hidden Markov Models, Hidden semi-Markov Models) to improve algorithm […]
The goal of this study is to design innovative algorithms that detect various types of driver distraction including cognitive, sensory-motor, and emotional distraction.
This series of naturalistic driving studies focuses on analyzing how drivers’ behavior changes as they are impacted by chronic health conditions such as obstructive sleep apnea (OSA), diabetes, and age related decline. The goal of the project is to design algorithms to comb through large amounts of driving data and identify changes in driving behavior […]