## 6 Conclusions  
A novel method for estimating vehicle sideslip angles using a neural network-based approach has been introduced. The outcomes of this research consist of a machine learning model where the inputs are typical vehicle dynamic signals: steering angle, lateral and longitudinal acceleration, yaw rate, and wheel speeds. These signals are accessible from the CAN-bus, as they are commonly measured in both motorsport and passenger vehicles. The analysis was conducted using various neural network architectures. This phase aimed to understand which net design, between feedforward, NARX, and recurrent neural networks, was more suitable for the prediction of the quantity of interest. The results showed that feedforward and NARX give very similar results but feedforward, having no feedback, is much faster. The worst was the recurrent neural network, both for accuracy and training time. These results arise because the feedback signal cannot exhibit significant delays to maintain real-time operational conditions and because the inaccuracies present in some parts of the estimate negatively interfere when going as input by means of the looping mechanism. The network's generalization capability was thoroughly tested across various scenarios, including multiple runs on different tracks while taking into account both new and worn tires. This testing approach aimed to assess the model's adaptability and robustness under various real-world conditions. By examining the network results on various circuits, each with unique characteristics, its ability to make accurate predictions across different track configurations was evaluated. The network demonstrated its ability to consistently estimate the angle of interest across different scenarios, showcasing its adaptability and suitability for various applications. Furthermore, the network was tested on both new and worn tires to account for variations in tire conditions, evaluating the model's solidity to changing tire properties throughout its life cycle. The results highlighted the network's remarkable capability to maintain accuracy and reliability, regardless of the tire's structural and viscoelastic properties, further underlining its real-world applicability. The neural network's great generalization performance across various circuits and tire conditions reinforces its potential for onboard applications, where it can provide real-time estimations of vehicle dynamics quantities with consistently high precision and reliability. For this purpose, the accurate results obtained by it when cascaded by another neural network that estimates longitudinal velocity are crucial. The methodology utilized in this study presents several important advantages, with one of the most prominent being its ability to provide accurate, near-instantaneous, real-time estimations of this fundamental physical quantity. The strong agreement between the estimated sideslip angle and the acquired one supports the suitability of these procedures for onboard implementation, rendering the need for bulky and expensive instrumentation obsolete. In summary, the machine learning model utilized in this study has low computational requirements and provides reliable outcomes, making it ideal for use in onboard control systems, such as ABS and TCS. In order to further enhance the benefits of the presented model, our next steps involve utilizing its outputs to feed vehicle dynamics models that can accurately calculate the slip and forces applied to the tire. These models will integrate with the driver assistance systems to further improve their precision so as to increase safety and performance. However, before implementing this, we plan to thoroughly test the model's functionality on various types of vehicles, in order to expand its use. **Author Contributions:** Conceptualization, M.R. and R.S. ; methodology, M.R. and F.T. ; software, G.N.D. ; validation, G.F. and G.A. ; formal analysis, G.A. ; investigation, F.T. ; resources, G.N.D. ; data curation, M.R.