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Sloped medians provide a run-off area for errant vehicles so that they can be safely stopped off-road with or without barriers placed in the sloped median. However, in order to optimize the design of sloped medians and the containment barriers, it is essential to accurately model the behavior of vehicles on such sloped terrain surfaces. In this study, models of a vehicle fleet comprising a small sedan and a pickup truck and sloped terrain surface are developed in CarSim™ to simulate errant vehicle behavior on sloped median. Full-scale crash tests were conducted using the vehicle fleet driven across a 9.754 meters wide median with a 6:1 slope at speeds ranging from 30 to 70 km/h. Measured data such as the lateral accelerations of the vehicle as well as chassis rotations (roll and pitch) were synchronized with the vehicle motion obtained from the video data.

The brake friction materials in an automotive brake system play important role in the overall braking performance of a vehicle. A previous study by the same authors was focused on wear testing for a 1040 steel disc interacting with Powder metallurgy (PM) copper-based brake lining material with and without MoS₂ additive at constant applied load and sliding velocity. In this paper, a non-Linear Autoregressive model (ARX) Model structure with sigmoid network having one hidden layer and nonlinear ANFIS (Adaptive Neuro-Fuzzy Inference System) model structure was used to find the best possible wear prediction results and both approaches have been applied to simulate wear behavior of the brake lining material. Preliminary results showed that ARX provides closer results to the experiments than the ANFIS model. As a result, nonlinear ARX modeling can be used as an effective tool in the prediction of brake lining material properties instead of time-consuming experimental processes.

Prediction of brake disc materials wear versus their formulation with brake operating conditions can play a critical role in the development of future brake disc materials. In this paper identification of the dry sliding wear behavior of magnesium (Mg) matrix (MMCs) reinforced with 0-3-6 wt % B4C particulates (B4Cp) was investigated. Wear tests were performed on a pin-on-disk configuration against SAE 1040 steel counter body under constant load and sliding speed. The wear resistance of composites was evaluated as a function of B4C particulates reinforcement. Identified models were based on experimental results. The wear load was considered as the input parameter, whereas the wear rate and friction of coefficient as the output parameter. A first order continuous-time linear model structure was chosen for the modeling. Simulations using the identified models were compared with experimental results and it was found that the modeling of wear process was satisfactory.

Analyzing the real road behavior of a vehicle in the most basic form unavoidably needs a proper driver model. In this study, a vehicle model with 10 degrees of freedom is used in conjunction with a fuzzy-logic-based driver model to generate vehicle's path through a number of specified points through which the vehicle must pass. The fuzzy controller aiming to imitate a real driver was modified to account for secondary sight characteristic of a human eye. Both longitudinal and lateral controls were applied in the study in providing necessary vehicle speed. While the response of the vehicle is assumed as a human driver's actions under controlled handling maneuvers, a fuzzy yaw control strategy was used to control the yaw motion of the vehicle by using independent braking inputs. Analysis was focused on the intention of human driver while providing the yaw moment change. A simulation is carried out in the Matlab© programming environment using a vehicle model in Simulink©.