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This paper discusses the importance of vibration transmitted from the ground to the driver from the perspective of human whole-body vibration (WBV). The scope of analysis is to compare the main vehicle frequencies with those important from the human vibration health and comfort point of view. That was performed by mapping the vibration transmissibility present in different sub sections of the vehicle. The first is the transmissibility between the axles and the chassis rail, the following between the chassis rail and the cabin. The last would be between the cabin and the drivers' seat, although that was not possible from the acquisition point of view. The vehicles measured have mechanical suspension and elastomeric cabin coupling. It is known that all suspension systems in vehicle are highly nonlinear, although here linear dynamic analysis methods were used.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.