Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data

Student competitions can play an important role in education: they promote interest and engagement of the students, as well as of the teachers. In the case of engineering, one of the most challenging contests in Europe is the Motostudent event, joined by the University of Brescia (UniBS) in 2016 for the first time. It is a typical implementation of Kolb's theory of experiential learning, where engineering theory and application meet in an intensive, 'hands-on' team work experience, resulting in a very effective learning process that involves the so-called soft skills as well. The paper aims at briefly reviewing the scope of competitions like the Formula SAE and sharing the authors' experience in a similar event, the Motostudent contest.

Indoor testing should reproduce the real-world environment in order to be effective. In this article, an efficient methodology to reproduce road profiles on a four-poster rig is presented: such a method includes a complex rig control strategy based on an iterative process. Road profiles come from a purposely designed set of sensors fitted on the car which remains the same regardless of the vehicle or surface type. Particular stresses such as speed humps, potholes and manholes can be reproduced as well. Since there are no previous similar studies, a validation is provided by comparing road and rig data streams and using the maximum absolute error and root mean square error as performance indexes. Results show that the rig is able to reproduce road profiles and the related inputs to the vehicle successfully; hence, the method is reliable and effective.