Current testing mandated by regulations relies on well-designed dummies. These dummies must be able to detect highly injurious situations as identified in real world crashes. The current study seeks to rank the severity of specific types of injuries – denoted by body region and skeletal/non-skeletal – in terms of threat to life and costs.
The data approach attempted to explore the questions: What types of injuries should The National Highway Traffic Safety Administration (NHTSA) strive to prevent; what measurements are required of a crash dummy to ascertain whether such injuries are sustainable in a crash test; and how many lives are likely to be saved under a given performance requirement to prevent such injuries? A comprehensive data set has been formed to address these issues including crash, vehicle, occupant, and injury parameters. The data set allows for identification of the most severe injuries based upon a variety of identifiers. Identification of the crash type, vehicle type, and Delta V, etc. was made for each case. It can be disseminated amongst researchers in a spreadsheet or database software file.
This current work provides an update of the data analysis component of the dummy development effort within NHTSA. Further, it will serve to introduce a new data set specifically tailored to the needs of the dummy developers, as well as researchers in the field.