Neural Network-Based Virtual Measurement of Road Vehicle Wheel Displacements

In the automotive industry, sensors play a crucial role in enabling the advancement of autonomous vehicles. However, physical sensors often present challenges due to their cost or practical limitations. Consequently, virtual sensors offer a viable and cost-effective alternative, where relevant metrics can be estimated using available data and mathematical models. This paper introduces a virtual sensor designed to measure the vertical displacement of a road vehicle’s wheels, specifically aimed at enhancing the control of the vehicle’s suspension systems. The virtual sensor utilizes a multi-output neural network to predict the displacements of all four wheels simultaneously. Inputs to the network include longitudinal, lateral, and vertical accelerations from an InertialMeasurement Unit (IMU), along with anti-roll torques from both front and rear anti-roll bars, and currents in the actuators of active suspensions.Experimental validationwas conducted using data collected from a Range Rover vehicle, demonstrating the efficacy of the virtual sensor in measuring vertical displacements of unsprung masses. Results were assessed through comparison with measurements obtained from a physical sensor, with Root Mean Square Error (RMSE) values confirming the accuracy of the virtual sensor’s estimations.