Database Integrity Validation for Driver Assistance
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Solution Overview
Problem
Existing database systems used by multiple vehicles face challenges in maintaining integrity, particularly with time-variant and time-invariant data, due to sporadic updates, faulty sensors, and vehicles traveling off-road, leading to unreliable route information and potential safety risks.
Innovation Solution
A method that compares sensor data from vehicles to the database, using data from multiple sensors to validate integrity and update the database only if consistent information is available, while identifying anomalous driving patterns and ensuring data accuracy through correlation between onboard sensing platforms and database information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the database is updated in real-time with data collected from vehicles, then the database reflects current road conditions and traffic, but the integrity of the database suffers due to bad data from faulty sensors and off-road driving
Solution Approach 1:
The patent merges data from multiple vehicle sensors to validate and update database information. By combining data sources, the system achieves more reliable updates while filtering out bad data from individual faulty sensors through cross-validation with other vehicles' sensor readings.
Solution Approach 2:
The system implements feedback mechanisms where sensor data from vehicles is continuously compared against database information, and updates are applied based on validated discrepancies. This feedback loop ensures that only verified accurate data modifies the database, maintaining integrity while enabling real-time updates.
2Reliability
If map data is updated sporadically, then the database maintains stability, but the data becomes unreliable and does not reflect current road conditions
Solution Approach 1:
The patent transitions from static, sporadic database updates to a dynamic update system that continuously incorporates validated sensor data from multiple vehicles. This dynamic approach allows the database to adapt to changing road conditions while maintaining reliability through multi-source validation.
Solution Approach 2:
The system enables continuous database updates by constantly processing and validating sensor data from vehicles. Rather than periodic updates, the database is continuously refined with verified information from multiple sources, ensuring current accuracy without sacrificing stability.
3Reliability
If data from multiple vehicle sensors is required to update the database, then the integrity can be validated, but the update process becomes more complex
Solution Approach 1:
The patent creates a universal validation framework that works across multiple vehicle sensors and data types. This multi-functional system handles various sensor inputs (GPS, accelerometer, gyroscope) through a common validation process, reducing overall system complexity despite dealing with multiple sources.
Data Source
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AI summary
A method of maintaining a database for a plurality of vehicles includes obtaining first sensor data from a vehicle sensor and comparing the first sensor data to data in the database. If the first sensor data does not match the database data, it is determined whether data from a single vehicle sensor or from a plurality of vehicle sensors is required to update the database. The database is updated if consistent data from a required number of vehicle sensors is available. A loss of database integrity is identified if data from a plurality of vehicle sensors is required to update the database but is not available. Anomalous driving is identified if data from a plurality of vehicle sensors is required to update the database and is available, and if the first sensor data is not confirmed by data from other vehicle sensors.