AV Location Data Detection via Geofence Filtering
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Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in efficiently detecting and processing large-scale location data due to privacy concerns and the high volume of data collected, which can lead to low recall and precision issues with off-the-shelf data classification tools.
Innovation Solution
A cloud-based data processing system with a geofence filter stage and a post-processing stage, utilizing regular expressions and trigram index-based searches, is implemented to filter and validate GPS location data, ensuring accuracy and scalability by checking for minimum precision and cross-schema consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If off-the-shelf data classification tools are used to detect location data, then the detection process is simple, but recall and precision are low
Solution Approach 1:
The patent segments the detection process into multiple specialized stages: geofence filtering stage that identifies records within geographic boundaries, post-processing stage that validates coordinate pairs, and cross-schema checking stage that ensures consistency across tables. Each stage focuses on specific aspects of location data detection, improving overall precision while maintaining operational efficiency through automated workflows.
2Device complexity
If traditional data processing methods are used, then the system is simple, but it cannot handle large-scale data efficiently
Solution Approach 1:
The patent introduces a multi-dimensional processing architecture that handles data across different dimensions: spatial dimension through geofence filtering using geographic coordinates, temporal dimension through batch processing of historical AV records, and relational dimension through cross-schema validation across multiple tables. This dimensional approach enables efficient processing of large-scale data without proportionally increasing system complexity.
3Reliability
If all collected data is processed, then complete detection is achieved, but processing time and resources increase significantly
Solution Approach 1:
The patent applies preliminary filtering actions before comprehensive processing by first applying geofence filters to identify only records within relevant geographic boundaries, then validating coordinate formats and precision thresholds. This preliminary action reduces the data volume requiring full processing while ensuring no relevant location data is missed, achieving complete detection of applicable records with reduced processing time.
Data Source
AI summary
A method for detecting AV location data includes receiving a data set from a data store storing data in connection with an autonomous vehicle (AV) fleet, the received data set comprising a plurality of table records; filtering the received data set by comparing data values of the plurality of table records with ranges of latitude values and longitude values and removing table records comprising data values outside both the range of latitude values and the range of longitude values; post-processing the filtered data set to identify tables that include a first table record comprising a data value within the range of latitude values and a corresponding second table record comprising a data value within the range of longitude values; and storing information regarding the identified tables in a data map, wherein the identified tables are tagged to indicate that the identified tables include location information.