title: Evaluation of Vehicle-Based Crash Severity Metrics.
abstract: OBJECTIVE
  Vehicle change in velocity (delta-v) is a widely used crash severity metric used to estimate occupant injury risk. Despite its widespread use, delta-v has several limitations. Of most concern, delta-v is a vehicle-based metric which does not consider the crash pulse or the performance of occupant restraints, e.g. seatbelts and airbags. Such criticisms have prompted the search for alternative impact severity metrics based upon vehicle kinematics. The purpose of this study was to assess the ability of the occupant impact velocity (OIV), acceleration severity index (ASI), vehicle pulse index (VPI), and maximum delta-v (delta-v) to predict serious injury in real world crashes.
  
  
  METHODS
  The study was based on the analysis of event data recorders (EDRs) downloaded from the National Automotive Sampling System / Crashworthiness Data System (NASS-CDS) 2000-2013 cases. All vehicles in the sample were GM passenger cars and light trucks involved in a frontal collision. Rollover crashes were excluded. Vehicles were restricted to single-event crashes that caused an airbag deployment. All EDR data were checked for a successful, completed recording of the event and that the crash pulse was complete. The maximum abbreviated injury scale (MAIS) was used to describe occupant injury outcome. Drivers were categorized into either non-seriously injured group (MAIS2-) or seriously injured group (MAIS3+), based on the severity of any injuries to the thorax, abdomen, and spine. ASI and OIV were calculated according to the Manual for Assessing Safety Hardware. VPI was calculated according to ISO/TR 12353-3, with vehicle-specific parameters determined from U.S. New Car Assessment Program crash tests. Using binary logistic regression, the cumulative probability of injury risk was determined for each metric and assessed for statistical significance, goodness-of-fit, and prediction accuracy.
  
  
  RESULTS
  The dataset included 102,744 vehicles. A Wald chi-square test showed each vehicle-based crash severity metric estimate to be a significant predictor in the model (p < 0.05). For the belted drivers, both OIV and VPI were significantly better predictors of serious injury than delta-v (p < 0.05). For the unbelted drivers, there was no statistically significant difference between delta-v, OIV, VPI, and ASI.
  
  
  CONCLUSIONS
  The broad findings of this study suggest it is feasible to improve injury prediction if we consider adding restraint performance to classic measures, e.g. delta-v. Applications, such as advanced automatic crash notification, should consider the use of different metrics for belted versus unbelted occupants.
authors.affiliation: a Virginia Tech , Biomedical Engineering and Mechanics Department , Blacksburg , VA.
authors.forename: Ada H
authors.initials: AH
authors.lastname: Tsoi
authors.affiliation: a Virginia Tech , Biomedical Engineering and Mechanics Department , Blacksburg , VA.
authors.forename: Hampton C
authors.initials: HC
authors.lastname: Gabler
mesh_terms: D015998:Abbreviated Injury Scale; D000054:Acceleration; D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001696:Biomechanical Phenomena; D005240:Feasibility Studies; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data*; D012622:Seat Belts / Q000706:statistics & numerical data; D015599:Trauma Severity Indices; D014481:United States; D014947:Wounds and Injuries / Q000209:etiology*
keywords: acceleration severity index; crash severity metric; delta-v, event data recorders; occupant impact velocity; vehicle pulse index