Bobbie Seppelt, Ph.D., M.S, is a Research Scientist in the Massachusetts Institute of Technology AgeLab & Center for Transportation and Logistics. She has over 15 years of experience in the assessment and analysis of driver behavior in the context of vehicle automation, attention management, and distraction, and has a research specialty in the integration and adoption of advanced technology within vehicles. She is a technical lead on two academic-industry partnerships: the Advanced Human Factors Evaluator for Attentional Demand (AHEAD) consortium, which is engaged in developing next generation driver attention measurement tools, and the Advanced Vehicle Technology (AVT) consortium, focused on understanding ‘in the wild’ driver use of vehicle technologies in production-level systems.
Dr. Seppelt is active in several national and international initiatives to identify and frame human-centered issues related to driving automation. She is chair of the SAE Committee on Automated Vehicles and DVI Challenges under the SAE Safety and Human Factors Steering Committee, a member of the joint ISO-SAE J3016 Working Group and of the organizing and editing committee for ISO TR21959 – a Human Factors WG, as well as an invited expert for the International EU–US–Japan Trilateral Automation in Road Transport Working Group. She is an author on over 30 technical contributions in transportation and related human factors areas.
Dr. Seppelt received her Ph.D. in Industrial Engineering (2009) from the University of Iowa, and her M.S. in Engineering Psychology (2003), B.S. in Psychology (2001), and B.S in Marketing (2001) degrees from the University of Illinois at Urbana-Champaign. She conducted postdoctoral research on driver trust and understanding of automated systems at the University of Wisconsin-Madison (2012). Prior to joining the AgeLab in early 2017, she worked as a research scientist at Touchstone Evaluations, Inc., an independent human factors and cognitive science research firm based in the Detroit area, in research areas primarily focused on driver-automation interaction, driver distraction, and interface demand assessment.