Computational models of cognition for human-automated vehicle interaction: State-of-the-art and future directions

Abstract

We discuss the state-of-the-art and future directions of the development, evaluation, and application of computational cognitive models for human-automated vehicle interaction. The capabilities of automated vehicles are rapidly increasing and changing human interaction with and around the vehicle. Yet, at the same time, fully automated vehicles that do not require human interaction are not available. Therefore, systems are needed in which the human and the vehicle interact together. We discuss how computational cognitive models that can describe, predict, and/or anticipate human behavior and thought can play a crucial role in this regard. Such research comes from many different disciplines including cognitive science, human-computer interaction, human factors, transportation research, and artificial intelligence. This special issue brings together state-of-the-art research from these fields. We identify four broader directions for future research: (1) to continue Allen Newell's research agenda for cognitive modeling, but now apply it to the field of human-automated vehicle interaction; (2) to move from isolated theory-slicing to integrated theories, (3) to consider cognitive models both for analysis of interaction and for use in embedded systems; (4) to move from models that mostly describe to models that can predict.