); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
2 Centro de Investigacion de Estudios avanzados del Moule (CEAM), Vicerrectoria de Investigacion y Postgrado, Universidad Catolica del Moule, Talca 3466706, Chile; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
2 Centro de Investigacion de Estudios avanzados del Moule (CEAM), Vicerrectoria de Investigacion y Postgrado, Universidad Catolica del Moule, Talca 3466706, Chile; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; [EMAIL_ADDRESS] (E.S. ); [EMAIL_ADDRESS] (S.B)  
[PERSON]  
1 Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile; eduardo. strict communication requirements of safety applications, which are vital for preserving human lives. Road safety applications have specified requirements concerning reliability and latency [4; 5]; therefore, it is necessary for the the underlying structure of the vehicle network to guarantee these standards. In VCSs, the particular propagation conditions of the vehicular environment make channel estimation and equalization a relevant challenge, especially because the high velocity of vehicles leads to variations in time and frequency [6]. In order to adapt to the harsh vehicular channel, the effective data rates of the IEEE 802.11p amendment were reduced to half in comparison to IEEE 802.11-based standards [7]. Traditional IEEE 802.11-based standards are the basis of Wireless Fidelity (WiFi) systems, which are commonly used in indoor scenarios and characterized by their low mobility [6; 7]. Despite the particular propagation conditions of the vehicular environment and the high mobility of the vehicles, no modifications were made to the IEEE 802.11p amendment regarding the channel estimation techniques adopted by traditional IEEE 802.11-based standards. A well-known channel estimation technique is the Least Squares (LS), which estimates the impulse response of the channel at the start of a frame [8]. LS works well for typical indoor Orthogonal Frequency Division Multiplexing (OFDM) systems; however, its performance is degraded in vehicular environments because the channel changes rapidly over time, which is more critical for higher velocities [6]. Another channel estimation technique corresponds to the Spectral Temporal Averaging (STA) [9], which uses time and frequency dependencies to properly perform the estimation stage. STA unfortunately suffers a great performance impact in non Wide-Sense Stationary Uncorrelated Scattering (WSSUS) channels. Unlike STA, the Constructed Data Pilots (CDP) technique [8] does not suffer significant performance degradation on non-WSSUS channels, but it has poor performance at low Signal to Noise Ratios (SNRs). Machine Learning (ML) provides a versatile set of tools to address the challenges of vehicular communications due to its ability to exploit available data and perform difficult tasks; for example, to predict channel parameters, to augment network throughput, and to help with congestion control or even autonomous driving [10].