Extracting Sections of Simulated Driving Routes that Elicit Driving Responses Predictive of ADHD via Recursively Constructed Ensembles

Résumé

In this paper we introduce a novel algorithm called Iterative Section Reduction (ISR) to automatically identify spatial regions wherein time series were recorded that are predictive of a target classification task. Specifically, using data collected from a driving simulator study, we identify which spatial regions (dubbed sections) along the simulated routes tend to manifest driving behaviors that are predictive of the presence of Attention Deficit Hyperactivity Disorder (ADHD). Identifying these sections is important for two main reasons: (1) to improve predictive accuracy of the trained ADHD screening models by filtering out non-predictive time series data, and (2) to gain insights into which on-road scenarios (dubbed events) elicit distinctly different driving behaviors from patients undergoing treatment for ADHD versus those that are not. Our experimental results show both improved classification performance over prior efforts and good alignment between the predictive sections identified and scripted on-road events in the simulator (negotiating turns and curves).