Vehicle Navigation Data Association for Ownership Change Detection
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Solution Overview
Problem
Existing vehicle navigation systems fail to detect changes in user associations within vehicle transportation networks, leading to potential unauthorized access to personalized data, especially during changes in ownership or operator identity.
Innovation Solution
A processor-based system that identifies vehicle transportation network information, associates it with a person based on operating data, and updates routes and access rights accordingly, using trajectory controllers to navigate the vehicle to new destinations based on changes in user identity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle navigation systems maintain personalized data associations without detection mechanisms, then data access continuity is preserved, but security risks increase during ownership changes
Solution Approach 1:
The system performs preliminary detection of ownership changes by analyzing vehicle probe data patterns before unauthorized access can occur. The processor continuously monitors transportation network information and identifies when a new owner is using the vehicle, proactively updating data associations before security breaches happen.
Solution Approach 2:
The system implements feedback mechanisms where the processor continuously analyzes vehicle operating data and transportation network information, compares it against stored patterns, and automatically updates data associations based on detected changes in ownership or operator identity, creating a closed-loop security system.
2Reliability
If the system continuously monitors vehicle operating data to detect ownership changes, then security is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential features from vehicle probe data that are indicative of ownership changes, such as transportation network patterns and destination preferences, rather than processing all raw data. This selective extraction reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The processor uses a universal analysis framework that handles multiple types of vehicle data (transportation network information, operating data, probe data) through a single pattern recognition system, reducing overall system complexity by avoiding multiple specialized processing modules.
3Adaptability or versatility
If the system updates transportation network associations in real-time, then adaptability to ownership changes is improved, but data processing time increases
Solution Approach 1:
The system pre-processes and stores vehicle probe data and transportation network information in structured formats, preparing it for rapid analysis. When ownership changes are detected, the pre-organized data allows for quick pattern matching and association updates, reducing real-time processing time.
Solution Approach 2:
The system dynamically adjusts its monitoring and processing intensity based on detected patterns. During periods of stable ownership, processing occurs at a lower intensity, while detecting changes trigger increased processing activity, optimizing the balance between adaptability and processing time.
Data Source
AI summary
A method and apparatus for associating a person with a portion of transportation network information based on vehicle operating information are described. The information includes a plurality of destinations. First vehicle operating information for a vehicle is identified, which information includes a first plurality of vehicle operations occurring during a first plurality of time windows. First values for a plurality of features are extracted from the first plurality of vehicle operations. A portion of the vehicle transportation network is associated with an owner/operator of the vehicle based on the first values. For consecutive time windows thereafter, second values for the features are extracted from second vehicle operating information for the vehicle that includes a second plurality of vehicle operations occurring during the consecutive time windows. Based on changes, a determination can be made as to whether an identity of the person associated with the vehicle has changed over time.