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Application of Advanced Telematics Data to Improve Post-Crash Care for Drivers and Passengers
Published December 01, 2008 by Fraunhofer Institut Chemische Technologie in Germany
Event: Airbag 2008
In the event of a crash where serious injuries occur, a rapid call for help followed by appropriate rescue care may significantly improve medical outcomes for drivers and passengers. Automatic Collision Notification (ACN) systems offer the opportunity to reduce the time to definitive care for those who could be injured during a crash. Current ACN systems rapidly transmit vital information including geographic coordinates and basic crash information plus they allow for voice communication between vehicle occupants and the Telematics Service Provider or TSP. In the US, approximately 500,000 BMWs are currently equipped with active ACN systems. Each month, approximately 600 of these vehicles are involved in crashes severe enough to automatically trigger a call for help. In about 12% of these cases, the vehicle information is transmitted and received, but no voice communication with the vehicle occupants is established. A critical question is: how many of these occupants are unable to communicate because of serious injuries?
Starting in September 2007, in many European countries and March 2008 in the US, new BMWs are equipped with enhanced ACN systems that transmit basic location and crash feature data plus additional crash characteristics compiled by in-vehicle sensor systems. These characteristics are important indicators of crash severity and include the crash energy commonly referred to as deltaV, impact direction, presence of the right front passenger, knowledge of three-point belt usage in front seats and the recognition of multiple impact crash events. The William Lehman Injury Research Center and BMW have pioneered the development of methods to process these crash characteristics to identify crashes in which there is a high probability of injury and a need for rapid post-crash response. This research has included the development and continued improvement of an algorithm called URGENCY.
The following paper describes the development and implementation of the URGENCY algorithm. Real-world crash example cases are also presented to demonstrate the functionality of the enhanced ACN systems and implementation of the URGENCY algorithm. Overall, findings have shown that crash injury predictions made using enhance ACN data and the algorithm would correctly identify 75.9% of the crashes with serious injuries and correctly discriminate 90.8% of those without serious injury. The precision of the identification of seriously injured occupants will be increased by other methods, including on-scene evaluation by rescue teams and remote verbal interviews by TSPs and 911. The URGENCY prediction will permit the early recognition of the need for rapid response to rescue a large fraction of crash-exposed people with time-critical injuries. Further, the data will help to improve on-scene triage decision making and help to target in-hospital diagnostics and treatment decisions.