Logo image
Back
Extracting spatiotemporal concentrations of discriminative features from driving simulator time series data
Dissertation   Open access

Extracting spatiotemporal concentrations of discriminative features from driving simulator time series data

David Grethlein
Doctor of Philosophy (Ph.D.), Drexel University
Jun 2026
DOI:
https://doi.org/10.17918/00011507
pdf
Grethlein_David_202665.13 MBDownloadView
PDF Open Access Open Access (License Unspecified)

Abstract

Driver classification Driving simulators Ensemble classifiers Multivariate time series classification Spatiotemporal modeling Machine Learning
A time series is a sequence of repeated numerical measurements taken over time. We employed novel methods for isolating and leveraging discriminative time series features (i.e., sub-series highly indicative of a class of time series samples) that were recorded during a simulated driving assessment, called the Virtual Driving Test (VDT), in order to develop a pre-screen for the on-road exam (ORE); identifying which license applicants that were about to take the ORE were most likely to fail the ORE (possibly dangerously so), based on their recorded VDT time series data. Our central hypothesis was that these discriminative time series features tended to be concentrated through time and space (i.e., spatiotemporally). We used Iterative Section Reduction (ISR), an ensemble-building algorithm that leveraged recursive data ablation to test for concentrations of discriminative time series features of various lengths recorded throughout the VDT planned route. ISR was used to build multivariate time series classification (MTSC) ensembles by systematically training and evaluating many section-specific VDT time series classifiers, dubbed modules, to act as voters in the ISR bagging ensembles in order to predict ORE outcome (pass/fail). Our most reliable MTSC models were ISR ensembles employing k-Medoids Dynamic Barycentric Averaging (k-Medoids DBA) modules using Dynamic Coordinate Locally Aligned Warping (DCLAW) as the time series distance (TSD) function for numerically comparing VDT time series samples to one another and predicting which samples were recorded by license applicants highly likely to fail the ORE (when taking it for the first time); achieving a composite evaluation risk ratio of 17.46 (14.59, 20.33). This was roughly four times as reliable as many commercially available breathalyzers are at correctly detecting if an individual's blood alcohol concentration (BAC) is over the legal limit. More than that, our ISR ensembles uncovered concentrations of discriminative time series features in the VDT data that suggested distracted driving combined with speeding through traffic light controlled intersections, turns, and curves in the roadway (particularly near a simulated playground where a child chased a ball into the roadway) could reliably distinguish license applicants that were highly likely to fail the ORE, particularly those that posed a potential threat to themselves or others through lack of sufficient practice before attempting the ORE, from all other applicants.

Metrics

2 Record Views

Details

Logo image