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Reducing Accelerometer Data from Instrumented Vehicles.

Michael O Bishop1, Jeffrey D Dawson1, Jennifer Merickel2

  • 1Department of Biostatistics, University of Iowa College of Public Health, 145 N. Riverside Drive, Iowa City, IA 52242.

Proceedings. American Statistical Association. Annual Meeting
|May 3, 2019
PubMed
Summary
This summary is machine-generated.

Standard deviation (SD) of vehicle acceleration is a more reliable metric for driving behavior analysis than a time series re-centering parameter. Naturalistic driving data showed higher reliability, highlighting challenges in comparing driving metrics across studies.

Keywords:
Cognitive AbilityDriving MetricsLateral AccelerationLongitudinal AccelerationNaturalistic DrivingReliability

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Area of Science:

  • Traffic and Transportation Engineering
  • Human Factors and Ergonomics
  • Automotive Safety

Background:

  • Vehicle acceleration data is crucial for understanding on-road driving behavior.
  • High-frequency acceleration data requires reduction to meaningful metrics over short driving segments.
  • Existing methods for analyzing driving behavior metrics may have limitations in reliability and comparability.

Purpose of the Study:

  • To compare the reliability of two methods for reducing vehicle acceleration data into meaningful driving metrics.
  • To assess the intraclass correlations (ICC) of these metrics derived from different driving conditions.
  • To evaluate the challenges in identifying and comparing driving metrics across various research platforms and data collection scenarios.

Main Methods:

  • Collected road test and naturalistic driving data from 65 subjects.
  • Isolated 24-second segments of accelerometer data.
  • Reduced data using standard deviation (SD) within segments and a time series re-centering parameter.
  • Analyzed data using random effects models to calculate ICCs, with and without speed adjustment.

Main Results:

  • The ICC of SD within a segment was consistently greater than the ICC of the re-centering parameter (0-30% vs. 0-1%).
  • ICCs from naturalistic driving data were generally higher than from fixed-route data (0-27% vs. 0-9%).
  • These findings suggest higher reliability of SD in naturalistic settings.

Conclusions:

  • Standard deviation is a more robust metric for quantifying driving behavior from acceleration data compared to the tested time series parameter.
  • Naturalistic driving environments may yield more reliable driving behavior metrics, reflecting typical individual behavior.
  • Careful consideration of data reduction methods, driving contexts, and epochs is essential for valid comparisons in driving behavior research.