Edge Local Dynamic Map Delivery for Low-Latency Vehicle Data
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Solution Overview
Problem
Existing local dynamic map (LDM) systems face limitations in providing real-time, highly dynamic environmental data to mobile devices, such as autonomous and semi-autonomous vehicles, due to sensor limitations and latency issues with data from remote network elements.
Innovation Solution
An Edge computing device generates and provides local dynamic map data to mobile devices by integrating first LDM data into an LDM data model, determining relevant second LDM data based on information from mobile devices, and transmitting this data in a format suitable for autonomous navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If LDM data is received from distant network elements, then coverage area is extended, but latency increases and data becomes less relevant to local conditions
Solution Approach 1:
The system segments the LDM data reception by establishing multiple Edge computing devices at different geographic locations. Each Edge device serves a local service area, dividing the overall coverage into manageable segments. This allows mobile devices to receive data from the nearest Edge device, minimizing latency while maintaining extended coverage through the distributed network of Edge devices.
Solution Approach 2:
Edge computing devices serve as intermediaries between mobile devices and the central server. The Edge devices receive data from the server and relay it to mobile devices in their local service areas, reducing the distance and latency for data transmission. This intermediary layer enables local data processing and distribution, improving response times while maintaining broad coverage.
2Loss of information
If mobile devices process all LDM data from sensors and other devices, then data completeness is improved, but computational burden increases
Solution Approach 1:
The Edge computing devices extract and process LDM data from multiple sources (sensors, other mobile devices, network elements) centrally, then provide the processed results to mobile devices. This extraction of processing functions from individual mobile devices to centralized Edge devices reduces the computational burden on mobile devices while maintaining data completeness through comprehensive data collection and processing at the Edge.
Solution Approach 2:
The Edge computing devices perform partial processing of LDM data, determining only the subset of data that is relevant to each mobile device's specific location and context. Rather than requiring mobile devices to process all possible LDM data, the Edge devices perform the filtering and selection, providing mobile devices with just the necessary portion of data needed for their operations.
3Area of stationary object
If LDM data is provided for large service areas, then coverage is improved, but data relevance to specific mobile devices decreases
Solution Approach 1:
The system implements local quality by having each Edge computing device provide customized LDM data tailored to the specific needs and location of mobile devices in its service area. The Edge devices determine which LDM data is relevant to each mobile device based on local conditions, ensuring high data relevance. This local customization is maintained across multiple service areas, with each Edge device optimizing data relevance for its local context while serving its designated geographic area.
Data Source
AI summary
Embodiments include methods performed by a processor of an Edge computing device for generating local dynamic map (LDM) data. The processor may receive new or updated (“first”) LDM data for a service area of the Edge computing device. The processor may integrate the first LDM data into an LDM data model. The processor may determine second LDM data of the LDM data model that is relevant to a vehicle or mobile device. The processor may provide the determined second LDM data to the vehicle or mobile device.