Causality effects of interventions and stressors on driving behaviors under typical conditions

Abstract

In this paper, we demonstrate that interventions and stressors do not necessarily cause the same distractions in all people; therefore, it is impossible to evaluate the impacts of interventions and stressors on traffic accidents. We analyzed publicly available multimodal data that was collected through one of the largest controlled experiments on distracted driving. A crossover design was used to examine the effects of actual and perceived interventions and stressors in driving behaviors and parallel designs on reactivity to a startling event. To analyze this data and make recommendations, we developed and compared a wide variety of mixed effects statistical models and machine learning methods to evaluate the effects of interventions and stressors on driving behaviors.