A theoretical model of human-automation interaction grounded in resource allocation policy during automated driving

Abstract

Automated driving systems have begun to allow drivers the ability to operate vehicles with reduced driver control. However, the literature in Human Factors indicates that automated systems of different types, purposes, and characteristics can often be used by human drivers counterproductively. This paper introduces an integrative model of human-automation interaction based on attentional resources and allocation policy to guide the systematic research on issues related to automated driving. The model builds upon a human information-processing model (Wickens et al., 2013) and focuses on the effective allocation of attentional resources to different perceptual, cognitive, and response stages when interacting with varying types and levels of automation (Parasuraman et al., 2000). The closed-loop mechanism allows drivers to evaluate joint human-machine performance and modulate the allocation policy, influenced by other factors. The model accounts for complacency and automation bias, and offers guidance of systematic research on drivers' attentional state during automated driving.