Edge Local Dynamic Map Filtering for Low-Latency Vehicle Data
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Solution Overview
Problem
Conventional local dynamic map (LDM) systems face limitations in providing highly dynamic environmental information to vehicles and mobile devices due to sensor perceptual limitations and latency in data received from remote network elements, leading to incomplete and irrelevant data processing burdens.
Innovation Solution
Implementing Edge computing resources to gather, process, and distribute LDM data closer to vehicles and mobile devices, leveraging greater computing power and proximity to provide highly dynamic information relevant to each device, including data aggregation and filtering of irrelevant data.
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 relevance decreases
Solution Approach 1:
The system segments the LDM data distribution by deploying multiple edge computing devices at different geographic locations. Each edge device serves a local area, providing data with minimal latency while collectively covering a broad region. This segmentation resolves the contradiction by enabling both wide coverage and low latency through distributed architecture.
Solution Approach 2:
The system adds a spatial dimension to data processing by distributing edge computing resources across multiple locations rather than relying on a single centralized server. This dimensional transformation enables the system to provide low-latency service to multiple geographic areas simultaneously, resolving the trade-off between coverage area and latency.
2Speed
If mobile device sensors are used to construct LDM, then real-time data is obtained, but perceptual limitations restrict data accuracy and completeness
Solution Approach 1:
The system merges data from multiple mobile device sensors with data from edge computing devices. By combining these diverse data sources, the system overcomes the perceptual limitations of individual sensors while maintaining real-time data acquisition capabilities. The fusion of multiple data streams improves both accuracy and completeness without sacrificing speed.
Solution Approach 2:
Edge computing devices act as intermediaries between mobile devices and the central server. These intermediaries aggregate and process sensor data locally, enhancing the accuracy and completeness of LDM data before transmitting it to mobile devices. This intermediary layer resolves the contradiction by improving measurement precision while preserving real-time data acquisition.
3Loss of information
If all received LDM data is processed by the mobile device, then comprehensive information is available, but processing burden increases
Solution Approach 1:
The system extracts and filters only the most relevant LDM data for each mobile device based on its location, trajectory, and contextual information. By taking out only the essential data needed for safe operation, the system reduces the processing burden on mobile devices while maintaining information completeness for critical navigation and safety functions.
Solution Approach 2:
The system provides customized LDM data tailored to each mobile device's specific needs, location, and operational context. Rather than providing all available data uniformly, the system adjusts the data quality and quantity locally for each device, reducing processing burden while ensuring each device receives the comprehensive information it specifically requires for its situation.
4Quantity of substance
If LDM data is transmitted from central server, then data aggregation is achieved, but transmission latency and network load increase
Solution Approach 1:
The system segments the data aggregation function by distributing edge computing devices throughout the service area. Each edge device aggregates LDM data locally for its surrounding area, eliminating the need for all data to traverse the entire network to a central server. This segmentation achieves comprehensive data aggregation while minimizing transmission latency and network load.
Solution Approach 2:
Edge computing devices serve as intermediaries between the central server and mobile devices. These intermediaries perform local data aggregation and preprocessing, reducing the volume of data that needs to be transmitted over the network. This intermediary approach achieves effective data aggregation while significantly reducing transmission latency and network load.
Data Source
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Figure 1B~1C
Figure 1D
AI summary
Embodiments include methods performed by a processor of an Edge computing device, such as an Edge Application Server (504), 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 (510). The processor may provide the determined second LDM data to the vehicle or mobile device (510).