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The use of pulse steering tests for assessment of handling qualities was investigated using a simulation of a comprehensive, nonlinear four wheel model of an automobile. Evaluations were conducted using frequency response functions of yaw rate and lateral acceleration obtained by FFT processing of the simulated response. In addition, as suggested by the work of Mimuro et al [1], four parameters (steady state yaw rate gain, yaw rate natural frequency and damping ratio, and lateral acceleration phase lag at 1 Hz) that characterize these response functions were also obtained by curve fitting techniques. The effects on accuracy of the response functions and the four parameters of variations in pulse shape, duration, and magnitude were investigated. Results from the simulated pulse steer test were compared with those from simulated swept sine steering tests.

The control of body roll on passenger vehicles can be used as a tool for controlling the “weight shift” that occurs during maneuvering. Distribution of load to the tires will determine the ability of each tire to generate lateral forces required for the maneuver and thus will significantly affect handling. In this investigation, the effects on weight shift and hence, on handling, of total roll stiffness, front to rear roll stiffness distribution, total roll damping, and roll damping distribution were examined. These results were then used to guide the development and analysis of several roll control algorithms. The results of the investigation indicate that roll control can be effective in improving handling and stability. However, simulation of the control algorithms showed that the controllers must be specifically tuned for the vehicle in which they are to be used.

The research objective was to measure and understand the preferred seat position of older drivers and younger drivers within their personal vehicles to influence recommended practices and meet the increased safety needs of all drivers. Improper selection of driver’s seat position may impact safety during a crash event and affect one’s capacity to see the roadway and reach the vehicle’s controls, such as steering wheel, accelerator, brake, clutch, and gear selector lever. Because of the stature changes associated with ageing and the fact that stature is normally distributed for both males and females, it was hypothesized that the SAE J4004 linear regression would be improved with the inclusion of gender and age terms that would provide a more accurate model to predict the seat track position of older drivers. Participants included 97 older drivers over the age of 60 and 20 younger drivers between the ages of 30 to 39.

A computational methodology has been developed for loss prediction in intake regions of internal combustion engines. The methodology consists of a hierarchy of four major tasks: (1) proper computational modeling of flow physics; (2) exact geometry and high quality and generation; (3) discretization schemes for low numerical viscosity; and (4) higher order turbulence modeling. Only when these four tasks are dealt with properly will a computational simulation yield consistently accurate results. This methodology, which is has been successfully tested and validated against benchmark quality data for a wide variety of complex 2-D and 3-D laminar and turbulent flow situations, is applied here to a loss prediction problem from industry. Total pressure losses in the intake region (inlet duct, manifold, plenum, ports, valves, and cylinder) of a Caterpillar diesel engine are predicted computationally and compared to experimental data.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

Driving skills and driving experience develop differently between a civilian and a military service member. Since 2000, the Department of Defense reports that two-thirds of non-related to war fatalities among active duty service members were due to transportation-related incidents. In addition, vehicle crashes are the leading non-related to war cause of both fatalities and serious injuries among active duty Marines. A pilot safe driving program for Marines was jointly developed by the Richard Petty Driving Experience and Clemson University Automotive Safety Research Institute. The pilot program includes four modules based on leading causes of vehicle crashes, and uses classroom and behind the wheel components to improve and reinforce safe driving skills and knowledge. The assessment results of this pilot program conducted with 192 Marines in September 2011 at Camp LeJeune, NC are presented and discussed.