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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.

The most popular semi-empirical models for predicting different aspects of the pneumatic tires performance under steady-state conditions are the Friction Ellipse Model (FEM) and the Magic Formula Model (MFM). The Friction Ellipse Model calculates the longitudinal and the lateral forces in the tire contact patch based on the slip ratio, the slip angle, the normal forces at the tire, and the friction coefficients between the tire and the road surface. The Magic Formula Model describes the cornering forces, the braking forces, and the aligning moment as functions of the slip ratio, the slip angle, and the normal forces at the tire.

The modeling of the terrain/road surface is a very important and challenging task in off/on-road vehicle dynamics simulation, road construction, and stochastic road surface assessment and identification. Various studies have been focused on on-road or on off-road terrain modeling, respectively. Most of these studies developed one-dimensional terrain/road models for specific applications. The approaches used to model paved surfaces differ, usually, from those employed to simulate unprepared terrain, since they must address different uncertainty ranges, linear or non-linear profiles, with normal or non-normal statistical distribution. From the practical point of view, one also needs to be able to reconstruct a representative terrain model from a limited set of measured data.

The understanding of wheel-soil interaction under longitudinal and lateral slip conditions is very important for off-road vehicle dynamics. However, understanding the physics of wheel-soil interaction is not easy, especially with uncertain operational environment and with the limitation of current measuring technique and hardware. This paper explores important aspects of off-road vehicle mobility using as a case study a 7 degree of freedom (DOF) vehicle model under steady-state cornering. In the evaluation of the vehicle response over a two-dimensional (2-D) terrain profile the load transfer due to cornering was taken into account. The tractive and the cornering vehicle capabilities were predicted using an algorithm that chooses the appropriate tire model (rigid or flexible) and finds the optimal geometry of the contact patch.

Currently, the final stages of chassis development are conducted on prototype vehicles, requiring vehicle manufacturers to dedicate copious resources to the development of each new vehicle platform. The objective of this work is to provide development engineers a system identification tool enabling them to use modeling and simulation to better estimate the required vehicle system parameters. This work develops a parameter identification method for existing vehicle models in which measured terrain data is used as the model excitation. The model was validated using a variety of excitation events and shown to provide accurate estimations of a vehicle’s roll, pitch, and vertical displacement.

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.

To study vehicle dynamics, we need to know the forces and moments acting on the vehicle. The most important forces and moments acting on the vehicle are generated at the tire contact patch. A semi-empirical tire model was developed at Advanced Vehicle Dynamics Lab (AVDL) to use for vehicle simulations for steady-state conditions. In this paper, we extended that model to account for transient conditions. We present the basic concept, the development of the tire model, and selective simulation results. The transient tire model is developed by including the effects of the vertical load variations due to the velocity and the acceleration to the tire characteristic parameters. The simulation was performed for the semi-empirical transient tire model in two scenarios. The vehicle driving and braking maneuver was simulated to present the transient longitudinal tire behavior. The vehicle lane changing maneuver also was performed to present the transient lateral tire behavior.

This paper uses the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) to estimate the population of front seat occupants exposed to far-side crashes and those with MAIS 3+ and fatal injuries. Countermeasures applicable to far-side planar crashes may also have benefits in some far-side rollovers. The near-side and far-side rollover populations with MAIS 3+ injuries and fatalities are also calculated and reported. Both restrained and unrestrained occupants are considered. Populations are subdivided according to ejection status – not ejected, full ejection, partial ejection and unknown ejection. Estimates are provided for the annual number of MAIS 3+ injuries and fatalities that occur each year in each category for the following belt use scenarios: (1) belt use as reported in NASS and (2) 100% belt use. In scenario 1, the exposure and casualties for the unbelted population are also shown. About 34% of the MAIS 3+F injuries in side crashes are in far-side crashes.