Tools for Transport: Driven to Learn With Connected Vehicles

Vehicle automation and assistance technologies have been touted as a means to reduce traffic collisions by minimizing or eliminating “error‐prone” and inefficient human operators. In concept, automation exists on a continuum that includes engaged driving by a human operator augmented by automated support features, vigilant driver monitoring of vehicle behavior with the possibility of driver take‐over, to full automation with no active monitoring by a human operator. Moreover, the degree of automation varies by vehicle features (e.g., lane centering, emergency braking, adaptive cruise control, parking), by setting, meaning that automated features may or may not be available depending on specific attributes of the traffic environment (e.g., traffic volume, road geometry, etc), and by implementation (e.g., haptic vs. auditory warnings). Thus, these automotive “transportation tools” are highly heterogeneous and pose unique challenges and opportunities for driver training. In this paper, we report the results of an experimental study (n = 36) to determine if enhanced vehicle feedback influences driver trust, effort, frustration, and performance (indexed by reaction time) in a virtual driving environment. Results are contextualized in the extant literature on learning to operate motor vehicles and outline key research questions essential for understanding the processes by which skilled performance develops with respect to a real‐world practical tool: the increasingly automated automobile. The automobile is a tool like no other. There is much excitement and enthusiasm for new and emerging transportation tools such as vehicle automation and driver assistance systems. Although there are high hopes for these technologies, there are many unknowns including the extent to which these new transportation tools can realistically and reliably improve driver safety and affect the subjective driving experience. This paper explores these questions in the context of evaluating collision warning systems on the behavior and perceptions of drivers with different amounts of experience and places the results in the greater context of tool use and skilled learning focusing on the adoption and utilization of mental models.