Motor vehicle crashes cause over 35,000 deaths and almost 3 million injuries per year. Automated vehicle technologies have emerged as a promising mechanism to prevent these crashes, to increase personal mobility, and to lower emissions. Delivery on these promises has been limited by a growing public concern over the safety of automated vehicles, particularly during transfers of control between automated systems and human drivers. Trust between human drivers and automated systems is a central concern during these interactions. Prior research in human-automation trust has established that the safety and performance of human-machine systems requires calibrated trust—a state where a human driver’s trust in an automated system matches the system’s capabilities. Trust calibration in automated vehicles is an elusive challenge because of limitations in trust measurement and methods that illuminate the impact of technology design decisions on trust and driver behavior. This project will promote the progress of science and advance the national health by advancing an understanding of human-automation trust. Specifically, the project will address the limitations of existing trust measures, model trust and driver behavior, and determine how autonomous vehicles that incorporate trust calibration models can influence dynamic trust and driving behavior. The approach will provide guidelines and technology design recommendations that could significantly reduce the human lives lost and injuries associated with vehicle crashes. Broader impacts of the work include undergraduate and graduate course development, focused research opportunities for underrepresented undergraduates at Texas A&M University, as well as student-leg outreach activities to local high school students.
The project will consist of three phases designed to (1) develop a novel and objective measure of dynamic trust using real-time measures of neural activation during AV interactions, (2) model driver interactions with automated vehicles along the spectrum of dynamic trust, and (3) validate the trust measure and driver behavior model with a trust calibration intervention-based driving simulation experiment. The measure development will be supported by data from human subjects studies in which drivers in a simulated automated vehicle will encounter a series of realistic driving scenarios designed to modulate trust in the system while physiological, neurological, behavioral, and subjective measures are collected. Neurological data will be analyzed with regression and connectivity analysis methods to identify correlates with trust states validated by the physiological and subjective measures. The correlates will be used to train and test novel process models of driver trust and responses to automation events that extend existing driver decision-making and control frameworks. The models and measure will be validated in a second driving simulation study including a trust calibration intervention.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
