title: Identification of safety-critical events using kinematic vehicle data and the discrete fourier transform.
abstract: Recent technological advances have made it both feasible and practical to identify unsafe driving behaviors using second-by-second trajectory data. Presented in this paper is a unique approach to detecting safety-critical events using vehicles' longitudinal accelerations. A Discrete Fourier Transform is used in combination with K-means clustering to flag patterns in the vehicles' accelerations in time-series that are likely to be crashes or near-crashes. The algorithm was able to detect roughly 78% of crasjavascript:void(0)hes and near-crashes (71 out of 91 validated events in the Naturalistic Driving Study data used), while generating about 1 false positive every 2.7h. In addition to presenting the promising results, an implementation strategy is discussed and further research topics that can improve this method are suggested in the paper.
authors.affiliation: University of Virginia, Department of Civil and Environmental Engineering, 351 McCormick Road, Charlottesville, VA, 22903, United States. Electronic address: rmk7su@virginia.edu.
authors.forename: Robert
authors.initials: R
authors.lastname: Kluger
authors.affiliation: University of Virginia, United States.
authors.forename: Brian L
authors.initials: BL
authors.lastname: Smith
authors.affiliation: University of Virginia, United States.
authors.forename: Hyungjun
authors.initials: H
authors.lastname: Park
authors.affiliation: University of Washington, United States.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Dailey
mesh_terms: D000054:Acceleration; D000063:Accidents, Traffic / Q000517:prevention & control*; D000465:Algorithms; D001334:Automobile Driving; D001696:Biomechanical Phenomena; D005583:Fourier Analysis; D006801:Humans; D012449:Safety
keywords: Crashes; Discrete fourier transform; Naturalistic driving study; Near-crashes; Safety-critical event