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We have recently obtained experimental data and used them to develop computational models to quantify occupant impact responses and injury risks for military vehicles during frontal crashes. The number of experimental tests and model runs are however, relatively small due to their high cost. While this is true across the auto industry, it is particularly critical for the Army and other government agencies operating under tight budget constraints. In this study we investigate through statistical simulations how the injury risk varies if a large number of experimental tests were conducted. We show that the injury risk distribution is skewed to the right implying that, although most physical tests result in a small injury risk, there are occasional physical tests for which the injury risk is extremely large. We compute the probabilities of such events and use them to identify optimum design conditions to minimize such probabilities.

A Computational Fluid Dynamics (CFD) study was conducted on four-vehicle platoons, and the aerodynamic data is then coupled with a high-fidelity truck simulation software (TruckSim) to determine fuel efficiency. Previous studies typically have focused on identical two vehicle platoons, whereas this study accounted for more complex platoon configurations. Heavy duty vehicles (HDVs), both military and commercial, make up a significant percentage of fuel consumption. This study aimed to quantify fuel savings of a platoon consisting of dissimilar trucks and trailers, thus reducing vehicle operational cost. The vehicle platoon featured two M915 trucks and two Peterbilt 579 trucks with dissimilar trailer configurations. An unloaded flatbed trailer, a centered 20 ft shipping container, two 20 ft shipping containers, and a 53 ft box trailer configurations were utilized.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.