Accurately estimating the lateral velocity of automatic ground vehicles is a complex task, especially when faced with sensor sampled measurements and unfamiliar mathematical models. In order to overcome these difficulties, the study presented here proposes a novel approach that makes use of a sampled data neural network observer. In order to fill in the information gap between successive samples, a compensating injector is introduced to the continuous state observer on which the observer is based. In order to replicate unknown dynamic vehicle systems, a radial basis function neural network is also implemented. A special weight update mechanism is used to update the weights continually. The Lyapunov methodology is used to demonstrate the stability of the suggested method. Experimental findings validate the effectiveness of the sampled data neural network observer, providing promising insights for improving lateral velocity estimation and enhancing the control and stability of autonomous vehicle systems.