Estimation of driver fatality ratio using computational modeling and objective measures based on vehicle intrusion ratio in head-on collisions
In the last decade, the increase in usage of light trucks and vans (LTVs) has resulted in an increase in fatal injuries to the occupants of passenger cars for truck-car accidents, because of the aggressive nature of LTVs. To study the aggressive behavior of LTVs, National Traffic Highway Safety Administration (NHTSA) has developed an aggressivity metric (AM) for different vehicles in a specific impact configuration. These AM however does not produce consistent estimates when specific vehicle-to-vehicle impact categories are studied. Hence, NHTSA has introduced a Driver Fatality Ratio (DFR), based on the Fatality Analysis Reporting System (FARS) and General Estimating System (GES) crash Involvement statistics, which has produced good estimates of the aggressive behavior of vehicles in crashes. The DFR proposed by NHTSA is based on the statistical data, which makes it difficult to evaluate DFR for other vehicle categories (e.g., crossovers, etc.), which are relatively new in the market as they do not have sufficient crash statistics. This research work proposes a new methodology based on computational reconstruction of impact crashes and objective measures to predict the DFR for any vehicle. The objective measures considered include the ratios of maximum intrusion, peak acceleration, and weight for the two vehicles in head-on collisions. Factors which directly influence fatal injuries to the occupants are identified and studied to develop a relation between these objective measures to the DFR. The proposed method is then validated for a range of LTVs against a passenger car, and is then used to predict the DFR for a cross category vehicle, a light pick-up truck, and a full-size car. Factors which influence these objective measures in predicting the DFR are discussed. Results from this study indicate that the ratio of intrusions produces a better estimate of the DFR and can be utilized in predicting fatality ratios for head-on collisions.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Mechanical Engineering.