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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.

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 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.

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.