Wireless Device Location Prediction Using Odometer Data
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Solution Overview
Problem
In the trucking industry, geolocation coverage is often spotty, leading to gaps in location data for vehicles, making it difficult to accurately track delivery vehicles using GPS, especially when the GPS receiver is not powered on or not in RF view of sufficient satellites.
Innovation Solution
A system and method for locating wireless devices that involves a device location server receiving a first and second location along with corresponding odometer values, determining a recommended distance, identifying candidate missing jurisdictions if the odometer value difference exceeds a threshold, and predicting a missing path by selecting a jurisdiction and predicting nodes within that jurisdiction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS tracking is used to locate wireless devices, then location tracking capability is improved, but location accuracy deteriorates when GPS receiver is not powered on or not in RF view of satellites
Solution Approach 1:
The patent uses intermediary data sources (odometer readings, historical path data, jurisdiction information) to bridge the gap when direct GPS measurement is unavailable. These intermediaries allow the system to infer location information indirectly through path prediction algorithms that combine multiple data sources to estimate device position when GPS is not functional
Solution Approach 2:
The system performs preliminary actions by collecting and storing historical path data, jurisdiction information, and odometer readings before GPS failure occurs. This pre-collected data serves as a foundation for predicting missing location segments, allowing the system to reconstruct paths even when GPS data is incomplete
2Duration of action of stationary object
If path prediction algorithms are used to fill gaps in location data, then location tracking continuity is improved, but system complexity increases
Solution Approach 1:
The patent segments the path prediction problem into distinct components: identifying candidate missing jurisdictions, predicting nodes within those jurisdictions, and reconstructing the complete path. This segmentation allows each sub-problem to be handled by specialized algorithms, improving overall system organization and maintainability while achieving continuous tracking
Solution Approach 2:
The system dynamically adapts its prediction approach based on available data quality and gaps in the trajectory. When GPS data is partially available, the system adjusts its path reconstruction methodology to combine direct measurements with predictive algorithms, optimizing the balance between accuracy and computational resources
Data Source
AI summary
First receiving a first location in a path of a wireless device and a second location in the path, a first odometer value associated with the device corresponding to the first location, and a second odometer value associated with the device corresponding to the second location, wherein the path comprises a set of ordered locations. Determining a recommended distance between the first location and the second location. Determining a magnitude of a difference between the first odometer value and the second odometer value. Determining that the magnitude is greater than a threshold. Identifying, in response to the third determining, at least one candidate missing jurisdiction. Presenting each candidate missing jurisdiction. Second receiving a selection of each presented identified candidate missing jurisdiction. First predicting at least one node in the path in the selected jurisdiction. Second predicting a missing path including the first location, each predicted node, and the second location.