Electronic Map Update via Telemetry Density Clustering
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Solution Overview
Problem
Conventional methods for updating electronic maps are labor-intensive and lack real-time accuracy, relying on manual data collection and prone to systematic errors from GPS data, which can lead to inaccuracies and delays in reflecting changes such as new road construction or misalignment of map features.
Innovation Solution
Collecting and aggregating telemetry data from mobile devices to identify missing map features by generating density maps and grouping telemetry probes into clusters, which are then used to update the electronic map, allowing for real-time updates without manual data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection is used to update electronic maps, then mapping accuracy can be improved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system uses existing mobile devices and their GPS capabilities to automatically collect and contribute map data, eliminating the need for dedicated manual data collection fleets. Users' devices self-service the map updating process by providing location data that is aggregated and processed to identify missing map features.
Solution Approach 2:
Mobile devices serve multiple functions: they are both consumer electronics that users operate and data collection instruments for map updating. The same device used for navigation and other purposes also contributes to map accuracy, eliminating the need for specialized data collection infrastructure.
2Reliability
If manual data collection is performed repeatedly to reflect road changes, then map accuracy is maintained, but the time and resources required increase significantly
Solution Approach 1:
The system continuously collects location data from mobile devices as users move through the environment, rather than performing discrete manual collection campaigns. This continuous data stream enables real-time detection of road changes and missing features, maintaining map reliability without periodic time-consuming updates.
Solution Approach 2:
The system proactively identifies missing map features by analyzing patterns in location data before manual verification is needed. By detecting clusters of location points that suggest missing roads or features, the system prepares map updates in advance, reducing the time required for subsequent verification and deployment.
3Quantity of substance
If GPS data is used to collect road position information, then map data can be obtained, but systematic errors from GPS can lead to misalignment and inaccuracies
Solution Approach 1:
The system aggregates location data from multiple mobile devices to identify missing map features. By combining data from many sources, random GPS errors cancel out and systematic patterns emerge, allowing accurate identification of roads and features even when individual GPS readings contain errors.
Solution Approach 2:
The system uses aggregated location patterns as an intermediary between raw GPS data and final map features. Rather than directly using individual GPS coordinates to define roads, the system first identifies clusters and patterns in the data, which serve as a mediator to filter out GPS errors and extract accurate road geometries.
Data Source
AI summary
A method for identifying missing map features in an electronic map, involving receiving telemetry probes indicating a geographic location of a mobile computing device, and identifying a subset of telemetry probes corresponding to an existing map feature. The identified subset is then removed from an aggregation of telemetry probes, and the remaining telemetry probes used to generate a density map and identify missing clusters of telemetry probes. A geometry of the missing clusters is determined, and a missing map feature defined from the geometry of the missing cluster. An electronic map may be updated with the missing map feature.