Peggy Wang

Job title: 
Research Engineer
Partners for Advanced Transportation Technology
Lead Researcher

Dr. Pei (Peggy) Wang has been an assistant research engineer at the California PATH program since June 2017. She received her Ph.D. in Human Factors and Ergonomics from Tsinghua University, and formerly worked at General Motors China R&D as an HMI researcher. She has been involved with research in cross-cultural studies of in-vehicle speech systems, data analytic studies for Apple Carplay usage, and driving styles studies for automated vehicles. Since her role began, she has led the human-factors team in PATH while conducting research projects funded by Caltrans, SB-1, and industrial consortiums. Her Human Factors research includes applying psychological and physiological principles to the design of systems, tasks, and environments in order to establish their successful and safe use. The focus of her research is to determine which aspects of road or traffic management devices that should be modified to improve driver performance and reduce unsafe behaviors. It is integral to shaping the design of road infrastructure and traffic management systems (e.g., signs, signals) in order to mitigate the associated safety challenge areas.

Dr. Wang has also explored the interaction between highly automated vehicles and pedestrians. Her team developed a prototype AV communication system (in the form of a mounted LED panel on an AV) for pedestrians that assisted in gaining their trust and confidence in the system while interacting with the AV.

Dr. Wang is currently working on evaluating driver compliance behaviors in addition to assessing drivers’ responses to eco-driving applications. Eco-driving applications are designed to change a person’s driving behavior by providing real-time, vehicle-specific information and advice such as to accelerate slowly and to reduce speed in order to optimize vehicle speed profile, reduce fuel consumption, and reduce emissions. Her research focuses on the effects on fuel savings, emission reduction, and the associated safety impacts by analyzing a simulated driving experiment involving a local roadway with signalized intersections and freeway stop-and-go traffic.