Automobile Crash Modeling and the Monte Carlo Method

The lack of a large number of crash data waveforms can limit the reliability of electronic Crash Detection Algorithms (CDAs). This paper discusses how statistics and the Monte Carlo (MC) method can be used to generate a large number of crash waveforms, and therefore increase CDA reliability. The MC method is used to model a crash waveform into two parts: 1) an underlying crash waveform, and 2) noise superimposed on the crash. The noise statistics are then varied and recombined with the underlying crash waveform to generate a large number of new crash waveforms. In addition Rough Road models were developed and concatenated with crash waveforms to better simulate real life. Finally a comparison between two CDAs was performed. The results show that one CDA is more robust than the other.