Abstract
Autonomous vehicles are rapidly becoming a central component of future mobility systems—but beyond enabling safe navigation, what else can we do with the rich data they collect? This PhD dissertation explores Autonomous Vehicles as Sensors (AVaS) for real-time traffic state estimation in urban Mobility-on-Demand (MoD) services. By treating AVs as mobile or parked, distributed sensors, the research addresses the challenge of sparse traffic information and shifts the reliance away from fixed infrastructure such as loop detectors. Comprising work on hybrid traffic estimation models, AV fleet simulation, and empirical validation across network scales, the dissertation culminates in the development of predictive, congestion-averse routing that links real-time traffic insights with proactive fleet control.
Yunfei is a PhD candidate at the Chair of Traffic Engineering and Control, Technical University of Munich (TU Munich), supervised by Prof. Dr.-Ing. Klaus Bogenberger. His research interests include Autonomous Vehicles, Traffic data analysis, and Mobility-on-Demand services. Before joining the group, he received his Bachelor’s degree in Civil Engineering from Tongji University in China and a Master’s degree in Transportation Systems from TU Munich. In his free time, he enjoys a variety of sports, including cycling, fencing, tennis, cardio strength training, skiing and others.
