Towards A Framework of Detecting Mode Confusion in Automated Driving: Examples of Data from Older Drivers

Abstract

A driver's confusion about the dynamic operating modes of an Automated Vehicle (AV), and thereby their confusion about their driving responsibilities can compromise safety. To be able to detect drivers’ mode confusion in AVs, we expand on a previous theoretical model of mode confusion and operationalize it by first defining the possible operating modes within an AV. Consequently, using these AV modes as different classes, we then propose a classification framework that can potentially detect a driver's mode confusion by classifying the driver's perceived AV mode using measures of their gaze behavior. The potential applicability of this novel framework is demonstrated by a classification algorithm that can distinguish between drivers’ gaze behavior measures during two AV modes of fully-automated and non-automated driving with 93% average accuracy. The dataset was collected from older drivers (65+), who, due to changes in sensory and/or cognitive abilities can be more susceptible to mode confusion.