Sensor Data Interpolation for Dynamic Map Integration
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Solution Overview
Problem
Conventional digital maps struggle to effectively integrate and utilize disparate dynamic data sources, leading to potential loss of beneficial information due to incompatibility and erroneous interpretation.
Innovation Solution
A method for gathering and processing sensor data from multiple sources with varying sequences and priorities, involving the receipt of estimated position points and path events, interpolation between data points, and alignment with geographic maps, while anonymizing and prioritizing data for real-time or delayed submission to enhance dynamic services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data from multiple disparate sources is integrated into digital maps, then the completeness and dynamic information of maps is improved, but data compatibility issues and erroneous interpretation occur
Solution Approach 1:
The patent introduces a standardized data processing intermediary layer that receives sensor data from multiple disparate sources, processes it through uniform protocols, and transforms it into a standardized format compatible with digital map systems. This intermediary layer acts as a mediator that ensures data compatibility and prevents erroneous interpretation while preserving the completeness of dynamic information from various sources.
Solution Approach 2:
The system applies parameter changes by transforming raw sensor data parameters into standardized map-compatible parameters. The processing system adjusts and normalizes data parameters from different sources to conform to a unified standard, enabling reliable integration without loss of information while maintaining data interpretation accuracy.
2Loss of time
If sensor data is processed in real-time with high priority, then the timeliness of dynamic services is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing system into multiple priority levels and processing queues. High-priority sensor data requiring real-time processing is separated from lower-priority data, allowing the system to handle time-critical information efficiently while managing overall processing complexity through structured organization of processing tasks.
Solution Approach 2:
An intermediary processing layer is introduced that manages the complexity of real-time data processing by implementing standardized protocols and priority-based routing. This intermediary structure simplifies the handling of high-priority data while maintaining system organization, reducing the perceived complexity for individual processing components.
Data Source
AI summary
A method is provided that includes: receiving a plurality of estimated position points, each estimated position point including a timestamp, where each estimated position point is an estimate of a position of a vehicle at a time respective timestamp; receiving on or more path events, where each of the one or more path events includes a timestamp and data from at least one sensor of the vehicle; generating a path from the plurality of estimated position points, where the estimated position points are arranged in order of ascending time represented by the respective timestamp; and interpolating between two of the estimated position points to determine a location corresponding to one of the one or more path events, where the timestamp of the one of the one or more path event corresponds to a time that is between the times represented by the timestamps of the two estimated position points.