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The road damage caused by heavy trucks is accentuated by the dynamic loads excited by roughness in the road. Simulation models of trucks are used to predict dynamic wheel loads, but special models are required for tandem suspensions. Parameter values to characterize tandem suspension systems can be measured quasi-statically on a suspension measurement facility, but it is not known how well they fit dynamic models. The dynamic behavior of leaf-spring and air-spring tandem suspensions were measured on a hydraulic road simulator using remote parameter characterization techniques. The road simulator tests were duplicated with computer simulations of these suspensions based on quasi-static parameter measurements to compare dynamic load performance. In the case of the walking-beam suspension, simulated performance on the road was compared to experimental test data to evaluate the ability of the walking-beam model to predict dynamic load.

This paper describes control system and psychological concepts enabling the development of a simulation model suitable for use in emulating driver performance in situations involving the longitudinal control of the distance and headway-time to a preceding vehicle. The developed model has mathematical expressions and relationships pertaining to the driver's skill in operating the brake and accelerator (“inverse dynamics”) and the driver's perceptual and decision-making capabilities (“desired dynamics”). Simulation results for driving situations involving braking and accelerating are presented to aid in understanding the research work.

Few studies have systematically examined the effects of truck and bus workstation geometry on driver posture and position. This paper presents methods for determining drivers' postural responses and preferred component locations using a reconfigurable vehicle mockup. Body landmark locations recorded using a three-dimensional digitizer are used to compute a skeletal-linkage representation of the drivers' posture. A sequential adjustment procedure is used to determine the preferred positions and orientations of key components, including the seat, steering wheel, and pedals. Data gathered using these methods will be used to create new design tools for trucks and buses, including models of driver-selected seat position, eye location, and needed component adjustment ranges. The results will also be used to create accurate posture-prediction models for use with human modeling software.