Human Driving Behavior when Interacting with Automated Vehicles and the Implications on Traffic Efficiency

Abstract

As automated vehicles (AVs) become more common, their influence on human-driven vehicles (HDVs) in mixed traffic is increasingly relevant. This dissertation explores how AVs affect HDV driving behavior—specifically car-following, overtaking, and gap acceptance—and how these behavioral adaptations influence traffic efficiency. Combining driving simulator experiments, field tests, and traffic microsimulation, the research provides insights into the dynamics of mixed traffic and their implications for infrastructure and AV deployment.