Explicit Forward Glance Duration Hidden Markov Model for Inference of Spillover Detection

Abstract

To better understand the effects of distracted driving on crash causation, forward roadway glance durations need to be carefully examined. Secondary tasks that impose high cognitive load lead to spillover effects that are moderated by the duration of the forward roadway glance within an alternation sequence involving both, in-vehicle and on-road glances. Spillover effects diminish the hazard anticipation ability of drivers. When alternating glances in a time series, the probability of detecting a spillover is invisible and the hidden state depends on the amount of time that has elapsed since the secondary task was initiated in the current state which is in contrast with the hidden Markov theory, where there is a constant probability of changing state given spillover detection in the state up to that time. No research estimates the probability of spillover detection in a time series with an explicit glance duration. In the current effort, we apply a semi-hidden Markov model where secondary task severity is used as an observation to infer hidden state and relax the assumption of constant state duration. Based on the reliable accuracy of the task itself, and the proposed model, different sequences of secondary task during various time window were tested for spillover detection. With a threshold of 50%, different forward roadway glance durations are required in each sequence associated with different types of secondary tasks.