Investigation of urban routes for autonomous transportation using 5G communication and LiDAR data

The increase in urban traffic has led to significant challenges, primarily congestion and accidents, many of which stem from human errors, both direct and indirect. Connected, Cooperative, Autonomous and Automated Mobility (CCAM) presents a promising approach to improve traffic efficiency and safety. Achieving these goals requires overcoming several obstacles, including the development of robust communication and data processing systems, advancements in vehicle technology. To fully realize the potential of Autonomous Vehicles (AV) in urban and suburban settings, it is essential to tackle infrastructure deficiencies and to provide an alternative way of navigation when Global Navigation Satellite Systems (GNSS) signals are inaccurate or unavailable. This can be achieved through the integration of technologies such as sensor fusion techniques that combine data from multiple sources like LiDAR (Light Detection and Ranging) and cameras. This research evaluates the readiness of the route for autonomous transportation. It specifically examines the reliability and performance speeds of supporting systems such as Cellular Vehicle-to-Everything (C-V2X) and Vehicle-to-Infrastructure (V2I). The findings indicate critical shortcomings in various areas that must be addressed to optimize functionality. The paper will conclude with recommendations for future research and advancements necessary to further optimize AV navigation both on this route and in broader contexts.