Developing Interpretable Predictive Models of Driver Situational Awareness in Conditionally Automated Driving

Abstract

Situational awareness (SA) among human drivers is important to understand for the advancement of vehicle automation. In conditionally automated driving scenarios, where thevehicle may approach its operational limits, the driver will be required to resume control within an appropriate time frame. This would suggest that real-time information about surroundings needs to be provided to the driver in a meaningful way while ensuring they are continually aware of their road environment. The goal of this dissertation is to develop real-time predictive models of driver SA, focus- ing not only on performance but also on interpretability, providing users with insight into the driving context. The pursuit of this goal involves a structured approach, encompassing the following key steps: 1) Identification of driver-specific and environmental predictors, 2) Exploration of the relationships between three distinct levels of SA and the identified predictors, 3) Development of SA predictive models with various feature selection methods, and 4) Comparative assessment of the performance and interpretability of the developed models. To accomplish the initial two objectives and collect data for the subsequent steps, two driving simulator studies were conducted with 40 and 56 participants respectively. These studies provide insights on the predictors needed for the real-time predictive models as well as the complex relationships among different levels of SA (perception, comprehension, and projection) and the predictors. The data obtained from these studies serve as the foundational resource for objective 3: the development of real-time predictive models and Objective and 4: comparative analyses of the models in pursuit of the research goal.