Vehicular communications are an important focus of studies for 5G applications and beyond. However, in a scenario with doubly-selective and highly variable channel characteristics, tracking the wireless channel to ensure communication reliability is one of the main goals to provide communication efficiency. Moreover, multicarrier modulation schemes usually employed in these scenarios are susceptible to nonlinear distortions caused by high power amplifiers (HPA) at the transmitter, impairing the channel estimation and detection capability of the receivers. In view of these requirements and challenges, in the present work we propose a low complexity estimator based on the long short-term memory (LSTM) network, followed by a neural network (NN) in order to improve the data-pilot aided (DPA) estimation. In addition, we propose a new technique to exploit the characteristics of the vehicular channel, by sampling the subcarriers used at the input of the LSTM. Thus, besides tracking the variations of the wireless channel, the LSTM network is also used to interpolate the channel estimates for all subcarriers. The simulation results show the superiority of the proposed scheme in comparison with other state-of-the-art schemes, especially in high signal-to-noise ratio (SNR) regimes. Furthermore, the proposed scheme significantly reduces the computational complexity due to the subcarrier sampling procedure.