Selective Sensor Data Processing for Map Database Updates
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Solution Overview
Problem
The challenge lies in efficiently processing vast amounts of sensor data from diverse sources to maintain accurate and current map data, particularly for semi-autonomous vehicles, as existing methods struggle with filtering relevant data and integrating it into map databases efficiently, leading to potential inaccuracies in lane geometry and object detection.
Innovation Solution
A method and apparatus that selectively process sensor data by receiving campaign management requests, converting data into a standardized format, aggregating and aligning it with existing map data, and updating the map database with consensus observations, thereby improving data efficiency and reducing processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensor data from diverse sources is processed to maintain accurate map data, then map data accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the processing workflow into distinct stages: data reception, quality filtering, subset selection based on campaign requests, standardized conversion, and map database updating. This segmentation allows the system to process only relevant data portions at each stage, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent extracts and processes only the subset of sensor data that satisfies campaign management requests and quality metrics, rather than processing all incoming data. This extraction principle filters out irrelevant data early in the pipeline, significantly reducing processing time while preserving map data accuracy.
2Loss of time
If all sensor data is processed to ensure current map data, then map data timeliness is improved, but processing costs increase
Solution Approach 1:
The patent applies partial action by processing only the necessary subset of sensor data required to maintain current map data, rather than processing all available data. This approach achieves timeliness requirements while reducing computational energy consumption and processing costs.
Solution Approach 2:
The patent changes the parameter of data selection criteria based on campaign management requests and quality metrics. By dynamically adjusting which data parameters are processed based on current map update needs, the system achieves timely updates with reduced processing costs.
3Reliability
If sensor data from multiple sources is integrated to improve map accuracy, then map data reliability is improved, but data integration complexity increases
Solution Approach 1:
The patent implements a universal standardized conversion process that handles data from multiple diverse sensor sources. This multi-functional conversion mechanism translates various data formats into a common standard, simplifying integration while maintaining reliability from multiple sources.
Solution Approach 2:
The patent introduces standardized data formats and quality filtering mechanisms as intermediary layers between diverse sensor sources and the map database. These intermediaries harmonize data from multiple sources, reducing integration complexity while preserving reliability.
4Measurement precision
If comprehensive sensor data processing is performed to detect lane geometry and objects accurately, then detection precision is improved, but processing load increases
Solution Approach 1:
The patent performs preliminary quality filtering and subset selection before detailed processing of sensor data. By pre-filtering data based on quality metrics and campaign requests, the system reduces processing load while preserving the precision needed for accurate lane geometry and object detection.
Data Source
AI summary
A method is provided for selective processing of sensor data, and more particularly, to parsing sensor data from a data stream to identify sensor data relevant to specific requests, and processing the relevant data based on the type of request. Methods include: receiving a campaign management request defining requested source data metrics; receiving source data from a plurality of sources within a geographic area; determining a subset of the source data that satisfies the requested source data metrics; processing the subset of the source data satisfying the requested source data metrics to obtain observations; and updating map data of a map database based on the observations.