Driver cognition and modelling

Abstract

Car drivers need to process sensory input providing detailed information about dynamic changes in the surrounding in order to drive safely to their destination. Modelling approaches aim at identifying the cognitive mechanisms that enable humans to drive safely. Additionally, these approaches aim at describing the state of the driver with regard to emotions, workload, or situation awareness that can impact the driving behavior. As such, models can be drawn on to identify human-cetnred requirements for information, assistance, and automation systems. One prominent example for theoretical approaches modelling cognitive mechanisms is the Adaptive Character of Thought Theory (ACT-R), which was drawn on to describe the steering behavior of humans. However, the theoretical approaches together with their implementations in models consider our world as two-dimensional-ignoring other traffic participants, distances, and speed. Within the scope of this presentation, it will thus be argued that a spatial module needs to be considered in both, theoretical approaches and implementations of models, in order to describe driving behavior appropriately. Additionally, it will be demonstrated how driver states such as frustration can severely impact the capacity of the spatial module thus biasing the human capability to reach the goal safely and efficiently.