Abstract

The highway safety related to vehicle operation on the road is a complex function of dynamic interactions between the vehicle, the driver and the environment. The safety dynamics of a heavy vehicle thus relates to not only its directional stability and control limits, but also the control performance limits of the driver. In view of their lower stability limits, the directional dynamics of articulated freight vehicles have been extensively investigated assuming either negligible contributions due to driver or perfect adaptability of the driver to the vehicle motion. In this dissertation, a number of analytical models of varying complexities are developed to study the lateral, yaw and roll directional performance of an articulated vehicle. Nonlinear analytical models to estimate the cornering properties of tire are derived using Magic Formula and neural networks. Neural network techniques are also applied to derive a nonlinear vehicle model. Based upon a comparative study of response characteristics of various models, it is concluded that a vehicle model based upon yaw-plane dynamics with limited roll degree-of-freedom of the sprung mass can effectively predict the directional response. Parameter sensitivity analyses are performed to identify design parameters that affect the directional dynamics of the combination most significantly. System identification techniques are applied to derive vehicle parameters known to be uncertain. Different analytical models of the human driver are formulated to identify the most effective feedback motion variables and to study the contributions due to driver's interactions with the vehicle. The reported data attained from different field and driving simulator studies are reviewed to identify a range of driver's control parameters, and the performance limits of the drivers. A comprehensive closed-loop driver-articulated vehicle model is formulated, incorporating driver's preview, prediction and compensation abilities, and various motion cues arising from the vehicle's directional motion. The analytical model is analyzed to study the control demands imposed on the driver as functions of selected operational and environmental factors. The control performance limits of three different drivers with varying skill levels are formulated and coupled with the vehicle model. The coupled model is analyzed to identify optimal vehicle design parameters, such that the resulting design can be best adapted to the driver's skill. The results show that the proposed vehicle design that can be adapted for the driver yields considerable performance benefits in view of directional dynamics performance and thus the associated highway safety.