Nowadays vehicular communication has become a widespread phenomenon, which will cause spectrum scarcity. By utilizing the cognitive radio in vehicular communication can be an effective solution for communication between vehicles. However, it requires robust sensing model for its efficient usage. Hence, vehicles sense the spectrum and deliver their sensed information to the eNodeB. For spectrum sensing, numerous number of vehicles can bring up overhead for the eNodeB. Grouping the vehicles into clusters is one of the most effective method to lower the burden for eNodeB. We have proposed a novel clustering method to enhance the performance of vehicular communication. The proposed method has formed the clusters using artificial intelligence. Our proposed method achieves the highest performance by forming a best group of cluster heads and by selecting finest cluster members using machine learning. The maximized throughput is achieved using proposed method for vehicular communication. Moreover, the clusters are formed in such a way that highest energy efficiency is attained.

如今，车辆通信已成为一种普遍现象，这将导致频谱稀缺。通过在车辆通信中利用认知无线电，可以成为车辆之间通信的有效解决方案。但是，它需要强大的感测模型才能有效使用。因此，车辆感测频谱并将其感测到的信息传递到eNodeB。对于频谱感测，无数的车辆可以为eNodeB带来开销。将车辆分组是减轻eNodeB负担的最有效方法之一。我们提出了一种新颖的聚类方法来增强车辆通信的性能。所提出的方法已经利用人工智能形成了集群。我们提出的方法通过形成一组最佳的簇头并通过使用机器学习选择最佳的簇成员来实现最高的性能。使用所提出的用于车辆通信的方法来实现最大的吞吐量。此外，以获得最高能量效率的方式形成簇。