Time-Dependent Probe Data Weights for Digital Map Updates
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Solution Overview
Problem
Existing digital maps are costly to produce and update due to the need for extensive data collection and processing, and they often contain inaccuracies and outdated information, especially as road geometry changes over time, with incremental algorithms unable to effectively remove old data that influences map refinement and extension.
Innovation Solution
A method that assigns weight values to probe data based on creation date and adjusts these values over time, allowing for the removal of line segments from digital maps when their weight falls below a threshold, using time-dependent weight adjustments and decay functions to strategically update and refine digital maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If incremental algorithms are used to update digital maps, then map refinement and extension can be performed without processing the whole network again, but old probe data continues to influence the map negatively and cannot be removed
Solution Approach 1:
The patent applies the dynamics principle by making the weight of probe data time-dependent rather than static. The weight automatically decays over time according to a decay function, allowing the system to dynamically adjust the influence of old data without manual intervention. This resolves the contradiction by enabling old data to be automatically de-emphasized while maintaining the incremental update efficiency.
Solution Approach 2:
The patent changes the parameter of data weight from a static value to a time-dependent variable that decays based on the age of the probe data. By introducing a decay function that modifies the weight parameter over time, the system can reduce the influence of outdated data while preserving the benefits of incremental algorithms for map updates.
2Measurement precision
If digital maps are produced and updated using traditional surveying and digitizing methods, then map data can be collected and processed, but the process is very costly and time-consuming
Solution Approach 1:
The patent uses probe data from GPS devices and mobile phones as a cheaper copy or alternative source to traditional surveying methods. Instead of deploying expensive surveying equipment and personnel, the system collects location data from everyday devices already in use, significantly reducing map production costs while maintaining acceptable accuracy through data aggregation and processing.
Solution Approach 2:
The patent leverages inexpensive GPS traces from consumer devices as disposable data sources. These low-cost location measurements from mobile phones and navigation devices replace expensive traditional surveying, allowing frequent map updates without proportional increases in cost. The system processes large volumes of cheap probe data to achieve accurate map representations.
3Productivity
If digital maps are created using probe data from GPS devices and mobile phones, then map production cost is reduced, but the maps contain inaccuracies and systematic errors from faulty input sources
Solution Approach 1:
The patent merges multiple independent probe data sources from different GPS devices and mobile phones to compensate for individual inaccuracies. By aggregating data from numerous sources and applying clustering algorithms, the system identifies common patterns and eliminates random errors, achieving higher overall accuracy than any single source could provide while maintaining cost efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where the map generation process continuously refines its output by analyzing probe data patterns, identifying systematic errors, and adjusting the digital map accordingly. The system uses feedback from matched and unmatched probe points to improve map accuracy over time, correcting inaccuracies while maintaining the cost benefits of using consumer GPS data.
Data Source
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AI summary
A method for improving and extending an existing digital road network and generating new networks from statistically relevant amounts of probe data recorded by GPS-enabled navigation devices. New probe data is matched to the existing digital map, then the data merged into the existing network using a time-dependent weight and/or accuracy-dependent weight. A recalculation date is established, and the weight value of a line segment and/or trace is adjusted as a function of the time span relative to the recalculation. The function may include setting a maximal time period divided into bins each having a respective weight reduction factor, or applying decay function. Through this technique, digital maps can be updated and extended without undue influence exerted by old trace data.