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Though the purpose of a vehicle's suspension is multi-faceted and complex, the fundamentals may be simply stated: the suspension exists to provide the occupants with a tolerable ride, while simultaneously ensuring that the tires maintain good contact with the ground. At the root of the familiar ride/handling compromise, is the problem that tuning efforts which improve either grip or handling are generally to the detriment of the other. This study seeks to set forth a clear means for examining the familiar ride/handing compromise, by first exploring the key ideas of these terms, and then by describing the development of content-rich metrics to permit a direct optimization strategy. For simplicity, the optimization problem was examined in a unilateral manner, where heave (vertical; z-axis) behaviour is examined in isolation, though the methods described herein may be extended to pitch and roll behaviour as well.

The following paper presents an outline of the current state of driver modeling along with the various methods that are employed in their development. In recent years, vehicle manufacturers have implemented various systems that, in some manner, improve the operation of their vehicles. Many of these systems include an electronically controlled device which is capable of making decisions based on the immediate conditions affecting the vehicle. Much of the influence to develop such systems stems from the issue of safety: in emergency situations the control device is capable of making a decision quicker than the driver and thus reduces the potential for some form of collision. Another motivating factor behind these systems is to improve fuel efficiency, specifically in regard to hybrid vehicles where more than one form of propulsion is used and such devices can aid the driver to operate in a more efficient manner.

The development of a collision severity model can serve as an important tool in understanding the requirements for devising countermeasures to improve occupant safety and traffic safety. Collision type, weather conditions, and driver intoxication are some of the factors that may influence motor vehicle collisions. The objective of this study is to use artificial neural networks (ANNs) to identify the major determinants or contributors to fatal collisions based on various driver, vehicle, and environment characteristics obtained from collision data from Transport Canada. The developed model will have the capability to predict similar collision outcomes based on the variables analyzed in this study. A multilayer perceptron (MLP) neural network model with feed-forward back-propagation architecture is used to develop a generalized model for predicting collision severity. The model output, collision severity, is divided into three categories - fatal, injury, and property damage only.