Automated methods of detecting driver distractions

Bryan L Reimer, University of Rhode Island


With recent advances in wireless and computing technology, distracted driving has become commonplace on the roadway, extending far beyond the cell phone to other distractions affecting driver attention. This research builds upon a previous study that collected eye positions from a driver completing some routine tasks in an automobile. Traditional methods of eye movement analysis relied on manual identification of patterns from recorded scene video. However, these methods are not efficient when processing the large data sets acquired to discriminate significantly between a drivers' behavior under different road and distraction conditions. In this work, new procedures and tools for the analysis of recorded eye positions are proposed for the collected data. To overcome the time demands for manual classification methods, automated methods of analysis are developed as the major focus of this research. ^ Signal analysis is considered as a method of identifying periods of eye movements that differ significantly from the “steady state”. Methods for the analysis of eye movements in a static setting are developed and used for studying distracted driving and other tasks occurring in a dynamic setting. In a dynamic setting, fixations, smooth and saccadic eye movements are identified from the recorder eye positions and manually mapped to the actions being completed. This research focuses on developing a software tool for examining the link between the location and time a subject allocates their attention on a particular region of the visual field. Examples detail the feasibility of analyzing various types of recorded eye positions including laboratory experiments, simulation and real road driving experiments. Finally, Hidden Semi-Markov models are proposed to model how a driver interacts with in-vehicle devices. ^

Subject Area

Engineering, Automotive|Engineering, Industrial

Recommended Citation

Bryan L Reimer, "Automated methods of detecting driver distractions" (2003). Dissertations and Master's Theses (Campus Access). Paper AAI3103721.