THE EFFECTS OF AUTOMATED VEHICLE SYSTEM-CERTAINTY ON DRIVERS' TRUST AND BEHAVIOR

As automated vehicle (AV) systems become increasingly more intelligent, understanding the complex interplay between drivers' trust in these systems and their resulting behavior is paramount for the successful integration of autonomous technologies into the transportation landscape. Currently, the effects of displaying AV system-certainty information, concerning its navigability around obstacles, on drivers' trust, decision-making, and behavior is underexplored. This thesis seeks to address this research gap and evaluate a set of dynamic and continuous human-machine interfaces (HMIs) that present self-assessed system-certainty information to drivers of AVs. A simulated driving study was conducted wherein participants were exposed to four different linear and curvilinear AV system-certainty patterns when their AV approached a construction zone. The certainty patterns represented the vehicle’s confidence in safely avoiding the construction. Using this information, drivers needed to decide whether or not to take over from the vehicle. The AV’s reliability and system-certainty were not directly proportional to one another. During the study, drivers' trust, workload, takeover decisions and performance, eye movement behavior, and heart?rate measures were captured to comprehensively understand of the factors influencing drivers' interactions with automated vehicles. Overall, participants took over in 41.3% of the drives. Results suggest that the communication of different system-certainty trends had a significant effect on drivers’ takeover response times and gaze behavior, but did not affect their trust in the system nor their workload. Ultimately, the results of this work can be used to inform the design of in vehicle interfaces in future autonomous vehicles, aiming to enhance safety and driver acceptance. By elucidating the intricate relationship between drivers' trust and behavior, this study provides valuable insights for both researchers and developers, contributing to the ongoing discourse on the human factors associated with the integration of autonomous technologies into the transportation ecosystem.