## 1 Introduction
Hybrid vehicular communication offers numerous benefits, including improved safety through the sharing of real-time information about road conditions and potential hazards, as well as enhanced traffic management and reduced congestion. Overall, hybrid vehicular communication is a promising sustainable solution  
Figure 1: Vehicular-to-Everything (V2X) use cases: Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) [2]. that has the potential to revolutionize the transportation industry, along with the openness to integrate new V2X technologies. In a hybrid V2X communication, based on the network statistics and positional information derived from the received C-ITS messages from other V2X nodes, an advanced wireless vehicular communication technology selection decision can be made for optimizing the data communication between V2X nodes. Such a decision-making capability on the V2X node is crucial and can help in reducing the network latency, optimizing the usage of wireless resources and enhancing the overall safety on the roads. However, until now, technologies are statically linked to specific use cases i.e., short-range direct communication technologies for safety related time-critical information and long-range infrastructure assisted cellular technologies for informative, non-safety and non-time-critical communication [4]. Hence, in this study, an intelligent algorithm for hybrid V2X communications is designed based on a set of selection criteria, implemented, and tested on a real life V2X testbed that is described in the later sections. Contrary to a vehicle making intelligent decisions, considerable latency can be incurred if vehicular data is first sent to a central cloud server where the decision on a technology is made and communicated back via the network to the respective V2X node. This may also add extra load on the backhaul network. Considering the projected [1] surge in intelligent and autonomous vehicles on the roads, the current network may encounter elevated loads, particularly impacting latency-critical applications. Hence, we propose an intelligent real-time statistics-driven V2X technology selection algorithm running on every V2X node. This intelligent approach enables low latency communication for safety-critical messages, such as collision avoidance warnings, emergency braking, and road hazard notifications. Such messages require immediate response and quick action to prevent accidents or reducing the impact of a potential collision. By laying the groundwork for next-generation connectivity, this study contributes to enhancing the safety and efficiency of future vehicular networks. The following are the main novelties/contributions of this paper:  
* This is the first study within the hybrid V2X domain, wherein three technologies are taken into consideration for hybrid V2X communications. Specifically, two short-range technologies, namely C-V2X PC5 and DSRC, along with the long-range 5G Vu are included. This comprehensive approach offers increased flexibility in the selection of the most suitable V2X technology. * A detailed intelligent hybrid V2X technology selection algorithm has been introduced to optimize the selection of V2X technology and adapting the C-ITS message generation rate. This algorithm takes into account various factors, including latencies associated with CAM receptions from different technologies, the speed of the V2X node, proximity to the neighboring V2X nodes, the type of C-ITS message, and the prevailing traffic density. * The hybrid V2X technology selection algorithm under consideration also incorporates a scenario involving the concurrent transmission of CAM and DENM. Furthermore, the adjustments necessary for intelligent decision-making in this context are thoroughly examined and discussed. * This study entails an experimentally driven investigation conducted in real-world conditions on a road, incorporating authentic environmental limitations and concurrent transmissions of CAM and DENM.