Citation

Olleja P, Bärgman J, Lubbe N. J. Saf. Res. 2022; ePub: ePub.

Copyright

(Copyright © 2022, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2022.08.011

PMID

unavailable

Abstract

INTRODUCTION: Developers of in-vehicle safety systems need to have data allowing them to identify traffic safety issues and to estimate the benefit of the systems in the region where it is to be used, before they are deployed on-road. Developers typically want in-depth crash data. However, such data are often not available. There is a need to identify and validate complementary data sources that can complement in-depth crash data, such as Naturalistic Driving Data (NDD). However, few crashes are found in such data. This paper investigates how rear-end crashes that are artificially generated from two different sources of non-crash NDD (highD and SHRP2) compare to rear-end in-depth crash data (GIDAS).

METHOD: Crash characteristics and the performance of two conceptual automated emergency braking (AEB) systems were obtained through virtual simulations - simulating the time-series crash data from each data source.

RESULTS: Results show substantial differences in the estimated impact speeds between the artificially generated crashes based on both sources of NDD, and the in-depth crash data; both with and without AEB systems. Scenario types also differed substantially, where the NDD have many fewer scenarios where the following-vehicle is not following the lead vehicle, but instead catches-up at high speed. However, crashes based on NDD near-crashes show similar pre-crash criticality (time-to-collision) to in-depth crash data.

CONCLUSIONS: If crashes based on near-crashes are to be used in the design and assessment of preventive safety systems, it has to be done with great care, and crashes created purely from small amounts of everyday driving NDD are not of much use in such assessment. Practical applications: Researchers and developers of in-vehicle safety systems can use the results from this study: (a) when deciding which data to use for virtual safety assessment of such systems, and (b) to understand the limitations of NDD.

Language: en

Keywords

ADAS; AEB; Crash avoidance; False positive; GIDAS; highD; SHRP2