Transformation of Vehicular Networks through Machine Learning: Challenges and Opportunities

Autonomous vehicles (AVs) are currently getting widespread attention and considered as a highly promising application of wireless communication. Due to the standout features of 5G/6G, it is capable of supporting V2X (Vehicular to Everything) communications and can fulfill the communication requirements. For the efficient vehicular ad-hoc network (VANET), lower latency and ultra-reliability are prime requirements. Next, larger handoffs, high interference, and dynamic traffic are the major obstacles to seamless connectivity. These issues can be effectively tackled through the concept of Machine Learning (ML). In this paper, we conduct a comprehensive survey of vehicular communication, discussing its major challenges, highlighting the transformative potential of ML algorithms, and addressing the implementation challenges of ML for creating a smooth vehicular network. Furthermore, we explore future research opportunities in this direction.