Vehicle AR Navigation Using Image Segmentation and LiDAR

The patent extracts only the essential navigation elements (lane markings, road signs, traffic signals) from the complete map data, storing them as discrete objects with minimal attributes. This extraction approach maintains navigation accuracy by preserving critical information while dramatically reducing the overall data volume that needs to be stored and processed.

The navigation data is segmented into distinct functional components: map data containing geometric information, object data containing semantic information about traffic elements, and sensor data containing real-time measurements. This segmentation allows the system to process only relevant data subsets for each navigation task, reducing computational complexity and data storage requirements.

The system performs partial processing by focusing computational resources only on processing and analyzing the specific map objects and sensor data relevant to the current navigation context, rather than processing complete map data. This partial action approach maintains location accuracy by concentrating computational effort on critical data subsets while reducing overall processing time.

Map data and object data are pre-processed and organized into structured formats with defined schemas before runtime use. This preliminary organization of spatial relationships and object attributes enables faster query execution and data retrieval during navigation, reducing real-time processing requirements while maintaining accuracy.