5G ENABLED SELF-DRIVING CARS – V2X ARCHITECTURE ANALYSIS AND SOLUTIONS TO OPTIMIZE PERCEPTUAL VISION

The need for fully functional vehicle-to-everything (V2X) communication is of paramount importance for the progress of self-driving technology. Vehicles equipped with driver assistance technology, such as all Tesla models, remain at SAE level 2 (Partial Driving Automation). Moving from level 2 to level 3 automation necessitates a significant amount of research and development. The reason behind this is because levels 0 to 2 are regarded as small steps, while levels 3 to 5 are considered rather substantial. Attaining level 3 then 4, then 5 demands stringent requirements and is dependent on Ultra Reliable Low Latency Communications (URLCC) and enhanced Mobile Broadband (eMBB), which we have not yet achieved. High degrees of autonomy require network connectivity, and even in the absence of network connectivity, vehicles must be able to communicate and exchange information utilizing the 5G New Radio (NR) sidelink. The purpose of this research was to identify the factors behind self-driving car accidents, to present the V2X architecture, and to explore the challenges and present solutions. Control plane latency and bandwidth constraints were recognized as two major challenges. The main contribution of this thesis is to serve as a reference for future development of V2X communication, to investigate the cause of accidents in self-driving cars, and then provide solutions such as 256-QAM OFDM to increase the symbol rate thus increasing speed of data communication or throughput. Increased symbol rates in V2X communication will assist to optimize the network and accommodate many more 3rd Generation Partnership Project (3GPP) advanced V2X use cases and help prevent accidents