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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ABSTRACT Most of the existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because

During a journey, motor vehicles are subjected to different types of irregularities in the pavement. Some of these irregularities are introduced for a specific purpose over a stretch of road to slow down the vehicle at certain road points. However, the influence of installed speed bumps reveals certain additional effects which must be deeply analyzed to ensure vehicle and pedestrian safety. In some cases, it has been found that even when driving over transverse bands at a speed below the legal limit, the vehicle is damaged or tires lose grip with the pavement, precluding any kind of braking or turning maneuvers. Such phenomena indicate that either this element is not properly sized or the location is not appropriate, becoming counterproductive for traffic safety. In order to analyze the influence of these irregularities on the different components of the vehicle and its occupants, a simulation program with MatlabTM has been developed. The validated developed tool takes into account several aspects of the vehicle dynamics, bump geometry, and vehicle speed. The proposed tool provides the best possible information to establish a set of guidelines for the proper design and installation of speed bumps in different roads.

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.