Review on the Real-Time Mapping of Autonomous Vehicles: Technologies and Challenges

Autonomous vehicles rely on real-time mapping systems to ensure effective navigation, avoid obstacles, and plan their paths accurately. This paper attempts a detailed review of the technologies, and challenges involved in real-time mapping for AVs. The core technologies comprise Lidar, radar, cameras, GNSS, and IMU as made use of in aggregate to build high-definition maps of the environment. Techniques such as SLAM and high-definition mapping can update the map continuously when deployed over dynamic environments. It is precisely this capability that qualifies AVs as appropriately responsive to reality changes. Yet, such is an extremely challenging task that falls within the broad scope of latent issues, resource utilization through computation, adaptability, and sensor reliability, then discussed several promising future directions for improving the real-time mapping capabilities of AVs, namely, edge computing and vehicle to everything (V2X) communication. These can bring a great advantage to improving the performance of AVs by processing vast quantities of real-time data extremely quickly.