Using Data Science to Identify Injury Risk Levels of Vehicle Passenger Population Segments

Vehicle designers face a challenge to design vehicle passenger restraint systems that account for variability in driver BMI, stature, age, and gender. One solution is to design an adaptive restraint system that accounts for these variations. However, it is difficult to assess how this combination of factors affect the injury risk level. We will present, how using the injury risk simulations, uncertainty quantification, and data science provides a method to segment the population and identify injury risk level within each segment. This provides designers with a powerful tool to create designs reducing injury risk for all passengers.
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