Digital Replica for Trustworthy Cooperative Autonomous Vehicles - Farahat, Hady (AUC Knowledge Fountain, 2026)
Trust in an automated system can be defined as confidence in a vehicle's reliability, safety, and predictability, which is essential for the acceptance and widespread adoption of fully autonomous vehicles (FAVs); without it, users might disengage from using autonomous vehicles or reject the technology altogether. Most of the previous research has focused on trust from an ego vehicle perspective. However, next-generation vehicles are becoming more autonomous and connected, relying on vehicle-to-vehicle technology and vehicle-to-infrastructure technology with no human intervention. Hence, trust becomes more complex and fragile as multiple agents interact with each other, and it might become harder to establish due to unexpected inter-vehicle coordination, diminishing human control, limited system transparency, and generalization effects where users might transfer their trust or distrust from one system to another. In such a complex environment, trust has to exceed ego vehicles and take into account other autonomous vehicles and their coordination abilities. This thesis hypothesizes that providing the user with more information regarding the environmental state, including information about ego vehicle intentions, other autonomous vehicles' intentions, cooperation agreements, and road conditions, can foster trust not only toward ego vehicles but also toward other cooperative robotic road users. This can be reached by visualizing vehicle-to-everything information through augmented reality interfaces. To test this, a within-subjects experiment was conducted in a Virtual Reality (VR) environment. A customized DReyeVR simulator was utilized to develop a digital replica, which enables the simultaneous mimicking of vehicle behavior, algorithms, and user interface within a VR setup. Participants experienced three interfaces: (A) no transparency, (B) system-level transparency showing Only the ego vehicle's intentions, and (C) environment-level transparency displaying cooperation intention, planned path by other vehicles, and infrastructure information. The results indicate that the interface that offered environment-level transparency at the cost of a higher mental effort enhanced trust in the ego vehicle and cooperating fully autonomous vehicles (FAVs). These results provide insight for designing interfaces for cooperative autonomous vehicles, fostering trust toward other cooperative agents, and preventing users' disengagement from the technology.