To trust or not to trust?: Assessment and calibration of driver trust in automated vehicles

Automated vehicles promise to improve road safety and increase travelling comfort. Yet, recent accidents have raised awareness of the challenges posed by the way humans interact with such systems. Many of these challenges present a common denominator: user trust. While a lack of trust may induce drivers not to use the automated vehicle’s functionalities, excessive trust can lead to dangerous outcomes, with drivers using the system in situations it cannot cope with. Successful and safe interaction between humans and automated driving systems requires trust calibration: the process of continuously aligning driver trust to the reliability of the automated driving system. To date, there have been few studies focussing on the way driver trust varies from situation to situation, and the way in which experience in a range of situations may lead to more appropriate trust levels. Studies of driver trust also face a number of methodological issues including a lack of on-road investigations, concerns regarding the validity of simulator-based research, and the lack of reliable real-time measurements for the assessment of user trust. This dissertation partially addresses these research gaps. Our findings show that, before on-road experience, drivers tend to overestimate the capabilities of vehicles equipped with Level 2 systems. After experiencing the vehicles in multiple scenarios, drivers had a better understanding of vehicles’ limitations, resulting in better calibrated trust. Following studies were performed in simulated environments. Through a first validation study, we made sure that our driving simulator elicited in our participants a strong “sense of presence” - the feeling of truly belonging in the virtual environment. A second study showed that users’ trust levels in specific situations did not consistently match evaluations of vehicle reliability given by engineers. Finally, results obtained through a third study showed that combining gaze behaviour with electrodermal activity provided an effective measure of trust in a simulated automated vehicle. The real question is not whether “to trust or not to trust” automated driving technology. What we should be asking is when an automated vehicle can be trusted, and what can be done to calibrate user trust with the actual capabilities of the system