Randa Radwan Samaha

Identifying and Analyzing Reference Scenarios for the Development and Evaluation of Active Safety: Application to Preventive Pedestrian Safety

To develop and evaluate active safety systems, reference scenarios need to be identified and analyzed. In this research, a methodology for the deduction of reference scenarios and a simulation approach to evaluate the effectiveness of those systems is presented. Using the example of preventive pedestrian safety, accident data of the United States and Germany were analyzed to identify representative scenarios. Data of both countries show similar results and a combined set of scenarios was derived which could function as specifications for new systems, input parameters for simulation models and representative test scenarios for real world and simulator testing.

Predicting the Injury Severity of Pedestrians in Frontal Vehicle Crashes based on Empirical, In-depth Accident Data

For proposed pedestrian protection systems, evaluation of safety benefits is required as an integral part of the design and optimization phases. Stochastic (“Monte-Carlo”) simulation techniques are currently being utilized to predict safety benefits in terms of physics; however, converting physics to human benefits requires injury and fatality risk models. To this end, multivariate predictive models for pedestrian fatalities and for injury severity on the ISS scale are estimated using the US Pedestrian Crash Data Study (PCDS). In addition to collision speed, which is the most important single explanatory variable, age, pedestrian physiological characteristics and vehicle parameters are significant multivariate predictors. The in-sample as well as out-of-sample predictive quality is remarkably high. The models are intended to provide an interface to large-scale stochastic simulation and virtual testing of proposed vehicle-based active safety systems for pedestrian protection.