1 AUTOMATED CRASH NOTIFICATION ALGORITHMS: EVALUATION OF IN-VEHICLE PRINCIPAL DIRECTION OF FORCE (PDOF) ESTIMATION ALGORITHMS Kristofer D. Kusano Graduate Research Engineer, Mechanical Engineering, Virginia Tech, Blacksburg VA, USA, e-mail: [email protected] Stephanie M. Kusano Graduate Research Engineer, Biomedical Engineering, Virginia Tech, Blacksburg VA, USA, e-mail: [email protected] Hampton C. Gabler Professor, Biomedical Engineering, Virginia Tech, Blacksburg VA, USA, e-mail: [email protected] Submitted to the 3 rd International Conference on Road Safety and Simulation, September 14-16, 2011, Indianapolis, USA ABSTRACT Automated Crash Notification (ACN) algorithms utilize telemetric data from vehicles involved in collisions to notify the appropriate emergency services with the aim to elicit the appropriate medical response. One vital piece of telemetri c data is the Principal Direction of Force (PDOF) of the collision, which can be determined from data stored in the Event Data Recorder (EDR). This study examined PDOF estimation algorithms using EDRs recovered from staged crash tests and real-world collisions. In crash tests, PDOF estimated by the measurements from t he EDR were comparable to laborator y-grade instrumentation. In 4 side impact crash tes ts, the average PDOF measured by crash test instrumentation and computed from the EDR differed by 7% on average. In some proposed ACN systems, real-world collisions, PDOF is used as a surrogate f or damage side of the cras h (i.e. front, side, rear) which may not be appropriate in many crashes. In 91 EDRs recovered from real-world collisions, oblique PDOF estimates near 45° could be interpreted as either frontal and side damage; 18 cases of 91 would have been misclassified as damage side using PDOF as a surrogate, which would have resulted in an incorrect assessment of injury risk. For example, using a previously developed ACN algorithm that predicts risk of serious injury in a crash, misclassifying the damage side as a frontal collision instead of a side collision would result in a risk prediction of 9% instead of 38%. Keywords: Event Data Recorders, Automated Crash Notification, Principal Direction of Force
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