Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data

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Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data

Perez, Miguel A. / Sudweeks, Jeremy D. / Sears, Edie / Antin, Jonathan / Lee, Suzanne / Hankey, Jonathan M. / Dingus, Thomas A.

Abstract

Highlights•Naturalistic data offers insight into crashes, but these events have to be found.•Finding these events can be resource-intensive; approaches vary in performance.•Different kinematic thresholds to detect crash events were developed and tested.•Sensitivity was generally low, but can be improved by modifying the thresholds.•Future improvements in these approaches are possible.

AbstractUnderstanding causal factors for traffic safety-critical events (e.g., crashes and near-crashes) is an important step in reducing their frequency and severity. Naturalistic driving data offers unparalleled insight into these factors, but requires identification of situations where crashes are present within large volumes of data. Sensitivity and specificity of these identification approaches are key to minimizing the resources required to validate candidate crash events. This investigation used data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) and the Canada Naturalistic Driving Study (CNDS) to develop and validate different kinematic thresholds that can be used to detect crash events. Results indicate that the sensitivity of many of these approaches can be quite low, but can be improved by selecting particular threshold levels based on detection performance. Additional improvements in these approaches are possible, and may involve leveraging combinations of different detection approaches, including advanced statistical techniques and artificial intelligence approaches, additional parameter modifications, and automation of validation processes.

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Dokumentinformationen

Titel :

Performance of basic kinematic thresholds in the identification of crash and near-crash events within naturalistic driving data

Beteiligte:

Perez, Miguel A. (Autor:in) / Sudweeks, Jeremy D. (Autor:in) / Sears, Edie (Autor:in) / Antin, Jonathan (Autor:in) / Lee, Suzanne (Autor:in) / Hankey, Jonathan M. (Autor:in) / Dingus, Thomas A. (Autor:in)

Erschienen in:

Accident Analysis and Prevention ; 103 ; 10-19

Erscheinungsdatum :

2017-03-05

Format / Umfang :

10 pages

ISSN :

0001-4575

DOI :

https://doi.org/10.1016/j.aap.2017.03.005

Medientyp :

Aufsatz (Zeitschrift)