## 1 Introduction
The relevance of this topic in particular has been proven by the very large number of papers that have appeared in the literature over the past 30 years, often involving other subjects such as vehicle dynamics, tyre modelling, theory of control, data fusion, instrumentation, human behaviour, and human-machine interaction. To give an example, [PERSON] already proposed a review on the theme back in 2007 [2]. Also, renowned authors such as [PERSON] [15; 16], [PERSON] [13], and [PERSON] [17] provide significant contributions that present varied techniques and achievements. From all of the above, the authors felt the need to propose an updated review of the most significant contributions in recent years. To date, among the many VSA estimation strategies proposed, none have succeeded to be considered the most effective or the most accurate. To provide clarity in the subject, we present an extensive review of the techniques, strategies, models, and methods that have been published in the literature. A total of 119 studies on this topic have been reviewed. Two categories of VSA estimation have been identified:  
* _Observer-based_[13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95]: This approach uses a vehicle reference model for state estimators. Results can be accurate either in steady-state or transient vehicle conditions. The need of a model implies that results are strictly related to model complexity, and to the knowledge of its parameters. The most challenging aspects are usually the description of the tyres and their interaction with the road surface. An exhaustive vehicle model for lateral dynamics is normally highly non-linear, with several parameters to be known. Moreover, using a complex non-linear model leads to a significant computational burden required to run the system with its state observer. Several kinds of observers exist in the literature, the Kalman Filter (KF) being the most used one. To enhance observed-based VSA estimation, GPS (Global Positioning System) technology can be used in combination with an observer [68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95]. GPS technology can determine the position of the receiver without numerical integration. Comparing data from at least four satellites, a GPS receiver is able to find its own global position, and its velocities are then derived using Doppler measurements [68]. As presentedlater on in this paper, this method already provides satisfactory results, and better results are likely to be achievable due to the expected increase in the accuracy and reduction in cost of the GPS receivers. On the other hand, GPS receivers present issues such as temporary signal unavailability due to surroundings such as trees, tall buildings, and road tunnels, as well as different working frequencies with respect to other sensors involved in vehicle dynamics control (i.e., accelerometers, gyros, etc.) [68]. * _Neural network-based_[96; 97; 98; 99; 100]: This method is specifically used to overcome the need for a vehicle model of any kind and its related complex set of parameters [101]. Artificial neural networks (ANN) are largely considered effective tools for system modelling, as they are suitable to model complex systems using their ability to identify relationships from input-output data pairs. They also offer decisive advantages such as adaptive learning, fault tolerance, and generalisation. Moreover, in recent years, the development of high speed computers encouraged the application of ANN, which has progressed very quickly. Using an ANN, the vehicle can be considered as a black box system, and only a conventional set of sensors is needed to train/feed the network.