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Safety and minimal transit time are vital during transportation of essential commodities and passengers, especially in winter conditions. Icy roads are the worst driving conditions with the least available friction, leaving valuable cargo and precious human lives at stake. The study investigates the available friction at the tire-ice interface due to changes in key operational parameters. Experimental analysis of tractive performance of tires on ice was carried out indoor, using the terramechanics rig located at the Advanced Vehicle Dynamics Laboratory (AVDL) at Virginia Tech. The friction-slip ratio curves obtained from indoor testing were inputted into TruckSIM, defining tire behavior for various ice scenarios and then simulating performance of trucks on ice. The shortcomings of simulations in considering the effects of all the operational parameters result in differences between findings of indoor testing and truck performance simulations.

Mission demands for U.S. military tactical trucks require them to transport a broad array of cargo types, including intermodal containers. The wide range of mass properties associated with these diverse cargo requirements has resulted in potential for steering stability issues. The potential for steering stability issues largely originates from the high mobility characteristics of single-unit military tactical trucks relative to typical commercial cargo carriers. To quantify the influence of cargo variations on stability, vehicle dynamics experiments were conducted to obtain steering stability measurements for a tactical cargo truck hauling a broad range of rigid cargo loadings. The basic relationship for the understeer gradient measure of directional response behavior and observed data trends from the physical experiments were used to evaluate the relationship between the steering stability of the truck and the mass properties of the cargo.

A long standing problem with heavy vehicle stability has been rollover. With the higher center of gravity, heavier loads, and narrower tracks (as compared to passenger vehicles), they have a lower rollover stability threshold. In this paper, a rollover stability control algorithm based on a two-degrees-of-freedom (DOF) and a three-DOF vehicle model for a two-axle truck was developed. First, the 3DOF model was used to predict the future Lateral load Transfer Rate (LTR). Using this LTR value, the dynamic rollover propensity was estimated. Then, a robust output feedback gain control rollover stability control algorithm based on the combination of active yaw control and active front steering control was developed. A H₂/H∞/poles placement multi-objective control strategy was developed based on the 2DOF reference model.

The increasing use and implementation of yaw and roll stability control in heavy trucks has contributed to an increased level of safety for truck drivers and other motorists. It has been shown that the combination of the stability control systems with a predictive model-based stability index can dramatically improve the truck stability and hence road safety. In this respect the authors introduced a new Total Safety Margin (TSM) using a fuzzy logic-based stability index. That methodology utilized a smoothed step and provided acceptable results; however, continuing development has shown that a right sigmoid membership function distribution would provide more complete coverage of the fuzzy space. Compared to the more common triangular membership function which is discontinuous when the membership grade equals one, sigmoid functions facilitate obtaining smooth, continuously differentiable surfaces of a fuzzy model.

The use of global positioning systems, or GPS, as a means of logistical organization for fleet vehicles has become more widespread in recent years. The system has the ability to track vehicle location, report on diagnostic trouble codes, and keep tabs on maintenance schedules thus helping to improve the safety and productivity of the vehicles and their operators. In addition, the increasing use and implementation of yaw and roll stability control in heavy trucks has contributed to an increased level of safety for truck drivers and other motorists. However, these systems require the vehicle to begin a yaw or roll event before they assist in maintaining control. The aim of this paper is to present a new method for utilizing the GPS signal in conjunction with the fuzzy based stability index to create a truly active safety system.

This paper presents a six degree of freedom full vehicle model simulating the testing of heavy truck suspensions to evaluate the ride comfort and stability using actual characteristics of gas charged single tube shock absorbers. The model is developed using one of the commercial multi-body dynamics software packages, ADAMS. The model incorporates all sources of compliance: stiffness and damping with linear and non-linear characteristics. The front and the rear springs and dampers representing the suspension system were attached between the axles and the vehicle body. The front and the rear axles were attached to a wheel spindle assembly, which in turn was attached to the irregular drum wheel, simulating the road profile irregularities. As a result of the drum rotation, sudden vertical movements were induced in the vehicle suspension, due to the bumps and rebounds, thus simulating the road profile.

Tire-pavement interaction noise (TPIN) is a dominant source for passenger cars and trucks above 40 km/h and 70 km/h, respectively. TPIN is mainly generated from the interaction between the tire and the pavement. In this paper, twenty-two passenger car radial (PCR) tires of the same size (16 in. radius) but with different tread patterns were tested on a non-porous asphalt pavement. For each tire, the noise data were collected using an on-board sound intensity (OBSI) system at five speeds in the range from 45 to 65 mph (from 72 to 105 km/h). The OBSI system used an optical sensor to record a once-per-revolution signal to monitor the vehicle speed. This signal was also used to perform order tracking analysis to break down the total tire noise into two components: tread pattern-related noise and non-tread pattern-related noise.