## 2 Related Work  
Dynamically changing wireless and physical environment in V2X communication demand efficient vehicular Radio Access Technology (RAT) selection. Because of this, numerous approaches have been proposed in recent years considering multi-technology enabled vehicular communication and RAT selection algorithms. A recent study in [5] considered the two short-range technologies i.e., ITS-G5 and C-V2X PC5, where ITS-G5 serves as a primary RAT, whereas C-V2X PC5 is used when ITS-G5 channel gets congested or if there is lack of transmission range. Reliability gains are seen with the constructive combination of ITS-G5 and C-V2X PC5. Two more recent studies in [6; 7] have considered Deep Reinforcement Learning (DRL)-based selection strategy between DSRC and C-V2X PC5 mode 3. Specifically, the selection is based on the channel load, SNIR and latency. Using simulations, the DRL-based approach has shown improvements in packet reception ratio, reliability, throughput, Channel Busy Ratio (CBR) and message duplication percentage. Authors in another simulation-based study in [8] have proposed a joint LSTM multi-criteria technology selection approach for DSRC and C-V2X PC5 mode 3. Among the available technologies, the focus is on minimizing the load on stochastic queues and maximizing the throughput using the LSTM prediction technique. The implementation of mode 3 in [6; 7; 8] necessitates additional infrastructure and control signals, with mandatory base station coverage. An evolutionary game-based technology selection approach is considered in [9] for selection between DSRC and C-V2X mode 4 with a goal to maximize the transmission throughput. Using packet size and distance between the transmitter and receiver as the selection criteria, a total of 16 population profiles are considered each having a separate population share. The payoff function is the average transmission throughput for the concerned strategy and results have shown payoff gains for the simulated scenario.While [9] exclusively focuses on short-range technologies, the proposed solution necessitates the computation of payoff at the base station. Additionally, the broadcasting of both payoff and population profile to the V2X node incurs an extra communication link. Studies in [5; 6; 7; 8; 9] exclusively focus on short-range technologies, potentially resulting in coverage limitations. Furthermore, these studies lack consideration for various C-ITS messages and the adaptation of message rates. The saturation of short-range technologies in dense traffic scenarios can adversely affect the performance of V2X applications. An adaptive RAT selection and vertical handover algorithm is presented in [10]. Only DSRC and Long Term Evolution (LTE) are considered in this simulation-based study where DSRC always serves as a primary RAT, whereas LTE acts as a secondary RAT. A simulation-based hybrid V2X study is performed in [11] consisting of C-V2X PC5 and DSRC where the emphasis is on the C-V2X mode 4 adaptive Semi-Persistent Scheduling (SPS) control. CAM delivery is ensured over C-V2X PC5 by adapting collision probability in each SPS period. The frequency of CAMs is controlled in an adaptive manner such that the C-V2X error probability limit for the CAM transmission is not exceeded. Simultaneous duplicated CAM are sent over DSRC if the proposed mechanism fails to satisfy the constraints of C-V2X collision probability. In order to facilitate applications related to V2X video streaming, a Quality of Service (QoS)-Aware video transmission approach over hybrid vehicular network is proposed in [12]. DSRC is considered as a primary RAT, whereas, the secondary RAT is LTE in this real-time experiments driven study. The secondary RAT is only used if the packet loss rate of primary RAT goes below a certain threshold. Scenarios with Line of Sight (LOS), non-LOS with buildings and non-LOS with vehicles are considered and Packet Delivery Rate (PDR) and Frame Delivery Rate (FDR) are monitored.