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Almost a million people are killed worldwide each year in motor vehicle crashes, over 42,000 of them in the U.S. Human/driver error (or induced error) is the most commonly identified contributing cause according to crash studies, especially studies conducted in the U.S. Accordingly, if crashes are to be reduced, a human-centered approach is needed. As part of its Intelligent Transportation Systems program, the U.S. Department of Transportation (U.S. DOT) is funding several major projects (e.g., VII, IVBSS) concerned with active safety, warnings, and communications. As part of these and other projects, several meta-issues have arisen that deserve further attention.

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.

Figures of merit describing the performance qualities of multiple-trailer vehicle combinations (for example, rearward amplification) are usually determined from either full-scale vehicle testing or computer simulation analysis. Either method is expensive and time consuming, and restricted in practice to organizations with specialized equipment and engineering skills. One goal of a recent study, conducted by the University of Michigan Transportation Research Institute and sponsored by the Federal Highway Administration, was to use basic vehicle properties to develop simple formulations for estimating the performance qualities of multiple-trailer vehicle combinations. Several hundred computer simulation runs were made using UMTRI's Yaw/Roll program. Five common double-trailer vehicle configurations (defined by trailer lengths and axle configurations) were studied. Each of the five vehicles was subject to fifteen parameter variations.

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.

Intelligent Vehicle Highway System (IVHS) represents the application of information technology to improve the effectiveness of the existing road transportation system resulting in increased safety and better traffic management [1,2]. In-Vehicle Safety Advisory and Warning System (IVSAWS) Program is a Federal Highway Admisistration project to develop a system that provides drivers with advance notification of roadway hazards. IVSAWS units will provide supplemental warnings in order to ameliorate hazardous scenarios (e.g., railroad grade crossings, slow moving vehicles) which are particularly hazardous and have remained hazardous despite traditional crash reduction techniques such as additional mechanical signing. Program aspects include hazard scenario identification, system architecture design, driver alert warning subsystem design, communication subsystem design, and proof of concept field testing.

Directional performance characteristics of heavy truck combinations are reviewed with respect to the influences of multiple axles and articulation points. The performance characteristics considered include steady turning, directional stability, and forced responses in obstacle avoidance maneuvers. The review provides useful insights to engineers interested in the handling and safety qualities of these types of vehicles.

The implications of restricting axle loads to preserve pavements while at the same time allowing gross combination weights over 80,000 pounds are examined with respect to the design qualities of the types of heavy trucks that might be developed. The proposed vehicles would have more axles than current designs thereby achieving higher gross combination weights with smaller axle loads. Design factors influencing mobility, productivity, preservation of the highway infrastructure, and performance in safety-related maneuvers are discussed.