Citation

Kusano KD, Gäbler C. Traffic Injury Prev. 2014; 15(7): 753-761.

Affiliation

Virginia Tech, School of Biomedical Engineering and Sciences , 440 Kelly Hall, 325 Stanger St (0194) , Blacksburg , 24061 , United States.

Copyright

(Copyright © 2014, Informa - Taylor and Francis Group)

DOI

10.1080/15389588.2013.871003

PMID

24433115

Abstract

OBJECTIVE: The objective of active safety systems is to prevent or mitigate collisions. A critical component in the design of active safety systems is the identification of the target population for a proposed system. The target population for an active safety system is that set of crashes that a proposed system could prevent or mitigate. Target crashes have scenarios in which the sensors and algorithms would likely activate. For example, the rear-end crash scenario, where the front of one vehicle contacts another vehicle traveling in the same direction and in the same lane as the striking vehicle, is one scenario for which Forward Collision Warning (FCW) would be most effective in mitigating or preventing. This paper presents a novel set of pre-crash scenarios based upon coded variables from NHTSA's nationally representative crash databases in the U.S. METHODS: Using three databases (NASS/GES, NASS/CDS, FARS, and NMVCCS) the scenarios developed in this study can be used to quantify the number of police reported crashes, seriously injured occupants, and fatalities that are applicable to proposed active safety systems. In this paper, we use the pre-crash scenarios to identify the target populations for FCW, Pedestrian Crash Avoidance Systems (PCAS), Lane Departure Warning (LDW), and Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) systems. Crash scenarios were derived using pre-crash variables (critical event, accident type, pre-crash movement) present in all three data sources. Results and CONCLUSIONS: This study found that these active safety systems could potentially mitigate approximately 1 in 5 of all severity and serious injury crashes in the U.S., and 26% of fatal crashes. Annually, this corresponds to 1.2 million all severity, 14,353 serious injury (MAIS3+), and 7,412 fatal crashes. In addition, we provide the source code for the crash scenarios as an appendix to this paper so that researchers can use the crash scenarios in future research.

Language: en