Verteidigung Dissertation: Driver's perceived risk in relation to automated vehicle behaviour. Evaluation and mitigation of perceived risk through simulator studies, computational models, and user interface design

This dissertation makes several contributions to the field of perceived risk research in AVs. First, it provides foundational insights into perceived risk, demonstrating the significant influences of driving conditions, manoeuvre uncertainties and individual personal characteristics. The computational perceived risk models demonstrate strong predictive power in perceived risk and offer a deep understanding of how perceived risk is shaped in dynamic driving conditions. Additionally, the rich dataset obtained in this dissertation, which includes event-based discrete data and time-continuous data on perceived risk, serves as a new and open resource for future perceived risk research. Lastly, the practical evaluation of the design of UI provided actionable recommendations in enhancing trust and perceived safety, particularly through manoeuvre information delivered using auditory modality. These contributions advance the understanding, modelling, and practical application of perceived risk in automated driving environments, supporting the broader acceptance and integration of AVs.

More details under: https://research.tudelft.nl/en/publications/drivers-perceived-risk-in-relation-to-automated-vehicle-behaviour