  For autonomous driving or advanced driving assistance, it is key to monitor
the vehicle dynamics behavior. Accurate models of this behavior include
acceleration, but also the side-slip angle, that eventually results from the
complex interaction between the tires and the road. Though it is an essential
quantity (e.g. for stability assessment), as opposed to accelerations, it is
not measurable through conventional off-the-shelf sensors. Therefore, accurate
side-slip angle observers are necessary for the proper planning and control of
vehicles. In this paper, we introduce a novel approach that combines
model-based side-slip angle estimation with neural networks. We apply our
approach to real vehicle data. We prove that the proposed method is able to
outperform state-of-the-art methods for normal driving maneuvers, and for
near-limits maneuvers where providing accurate estimations becomes challenging.
