Use of an automatic crash notification system to relate dynamic vehicle data with occupant injuries

The objective of this study was to describe our initial experience with an automatic crash notification device (ACND) and to compare dynamic vehicle data acquired by the ACND in motor vehicle crashes (MVCs) for occupants with and without cervical strain injuries. Eight hundred and seventy-four cars were equipped with an ACND, which detected crashes by analyzing vehicular acceleration in real time. The device placed an automated call to 9-1-1 whenever the pre-established crash threshold was exceeded and transmitted crash location, principal direction of crash force, and crash change in velocity. All occupants involved in an MVC involving an ACNDequipped vehicle were contacted and asked to report anatomical location(s) of any injuries. Those with cervical- strain-type complaints were identified through post-crash interviews and medical record reviews. Principle direction of force and crash change in velocity were compared between these two groups. Dynamic vehicle data were obtained for 15 crashes involving 26 occupants, with crash change in velocity ranging from 12 kph to 42 kph. The principle direction of force was 12 o’clock (six vehicles), 2 o’clock (three vehicles), 3 o’clock (two vehicles), 6 o’clock (one vehicle), 9 o’clock (one vehicle), and 11 o’clock (two vehicles). Thirteen occupants reported a variety of injuries. Five reported cervical-strain-type complaints including three in a rear-end crash (principle direction of force 6 o’clock, change in velocity 29 kph), one in a frontal crash (principle direction of force 12 o’clock, change in velocity 14 kph), and one in a right-frontal crash (principle direction of force 2 o’clock, change in velocity 26 kph). Results indicate that, although the number of MVCs was small, no cervical-strain-type complaints were reported when change in velocity was less than 14 kph. Dynamic vehicular information obtained from the ACND at time of crash may be useful for instantaneous injury prediction. The ability to predict injury in real time may some day allow for better allocation of on-scene resources.