Reducing Accelerometer Data from Instrumented Vehicles.

Abstract

In on-road driving behavior studies, vehicle acceleration is sampled at high frequencies and then reduced to meaningful metrics over short driving segments. We examined road test data from 65 subjects driving over a common route, as well as driving in naturalistic situations using their own vehicle. We isolated 24-second segments, then reduced the accelerometer data via two methods: 1) standard deviation (SD) within a segment, and 2) re-centering parameter from a time series model previously developed for driving simulator data. We analyzed the data via random effects models to ascertain the intraclass correlations (ICC's) of the metrics. With and without adjusting for speed, the ICC of SD within a segment tended to be much greater than the ICC of the re-centering parameter for the segment (range: 0-30% vs. 0-1%). Also, ICC's from the naturalistic driving data tended to be greater than the fixed-route data (range: 0-27% vs. 0-9%), which could reflect individuals exhibiting their more usual driving behavior in naturalistic environments. Findings illustrate the challenges of identifying meaningful driving metrics and comparing these across different epochs, road segments and research platforms.