The intelligent transportation system (ITS) along with vehicular communications are making our daily life safer and easier e.g., saving time, traffic control, safe driving, etc. Many transmission mode selection and resource allocation schemes are mitigating to full the quality of service requirements with minimum latency and negligible interference. For message transmission to far away vehicles realistic cellular link and mode selection is a bottleneck, however, for nearby devices, the safety critical information needs V2V links. Reinforcement learning (RL) and Deep learning (DL) have reshaped vehicular communication in a new model where vehicles act like human beings and take their decisions autonomously without human intervention. In our work, we investigate Vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure where each link takes a decision to find the optimal subband and power level. We investigated the case where each V2V link connected with Vehicle-to-Infrastructure (V2I) satisfying stringent latency constraints while minimizing interference. We exploit RL with DL to develop highly intelligent performance where agents effectively learn to select mode and share spectrum with V2V and V2I.