This article presents a novel estimator based on sensor fusion, which combines the Neural Network (NN) with a Kalman filter in order to estimate the vehicle roll angle. The NN estimates a “pseudo-roll angle” through variables that are easily measured from Inertial Measurement Unit (IMU) sensors. An IMU is a device that is commonly used for vehicle motion detection, and its cost has decreased during recent years. The pseudo-roll angle is introduced in the Kalman filter in order to filter noise and minimize the variance of the norm and maximum errors’ estimation. The NN has been trained for J-turn maneuvers, double lane change maneuvers and lane change maneuvers at different speeds and road friction coefficients. The proposed method takes into account the vehicle non-linearities, thus yielding good roll angle estimation. Finally, the proposed estimator has been compared with one that uses the suspension deflections to obtain the pseudo-roll angle. Experimental results show the effectiveness of the proposed NN and Kalman filter-based estimator.

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The dynamic simulation of mechanical systems is an essential tool in vehicle design. This work analyses the influence of a shock absorber model on a vehicle's dynamic behaviour by means of a simulation-based model. The real behaviour of a European mediumrange car shock absorber has been obtained by means of a test rig. From the damper's real behaviour, three mathematical models were generated, increasing the complexity. An existing full vehicle simulation application (CarSim TM ) was used for this particular study. The vehicle's behaviour was analysed for typical driving manoeuvres taking into account lateral, vertical, and longitudinal forces and was compared with the results obtained with the different shock absorber models developed. As a result of this paper, it was demonstrated that, in order to obtain results with an acceptable level of accuracy, it is not necessary to rely on extremely complex shock absorber models.

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