Cloud-Orchestrated Real-Time HD Map Regeneration for Autonomous Vehicles

High-definition (HD) maps are vital in facilitating safe and precise navigation of autonomous vehicles since they give detailed content concerning road geometry, road signs, and lanes. This is, however, a major problem when it comes to keeping current maps of HD in an environment that is constantly changing and where periodic updates cannot keep up with real-time changes. The main idea presented in this paper is the implementation of a cloud-native, AI-based HD map regeneration system that allows detecting a change in the environment and patching the old map blocks in real-time. By using AWS cloud computing (Kinesis data streaming, SageMaker model deployment, and S3/DynamoDB storage/versioning), the proposed system can provide low-latency map updates, which are highly accurate and have high scalability. Experimental results indicate that this solution enables more frequent map updates with significantly lower latency and reduced resource consumption compared to conventional methods. It also enhances the reliability of autonomous navigation in dynamic, real-world environments.