Vehicle Anomaly Detection Using Cross-Referenced Sensor Sets
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Solution Overview
Problem
The increasing complexity of vehicles, particularly with the trend towards autonomous driving, makes it difficult for users to diagnose anomalies caused by component failures or data manipulation, as existing self-diagnostic systems struggle to discern between these two issues without expert knowledge.
Innovation Solution
A system utilizing a plurality of sensors, processors, and system memory to group vehicular data into detection sets, compare signals to a normal behavior model, and cross-reference detection sets to identify anomalies, distinguishing between component failures and data manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-diagnostic systems are made more complex to assist mechanics without requiring expert knowledge, then ease of operation is improved, but device complexity increases and requires more communication interfaces
Solution Approach 1:
The patent introduces an intermediary system that includes a server and database acting as mediators between the vehicle's communication interfaces and the diagnostic analysis. This intermediary handles the complex data processing, pattern recognition, and expert knowledge requirements, allowing the onboard system to remain relatively simple while still providing expert-level diagnostic capabilities through the communication interface.
2Adaptability or versatility
If the vehicle includes more communication interfaces to support complex self-diagnostics and autonomous components, then adaptability is improved, but vulnerability to data manipulation increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors data from multiple communication interfaces, compares it against expected patterns and models, and uses this feedback to detect anomalies or manipulations. The system provides feedback loops that allow real-time validation and cross-checking of data integrity across different communication channels.
Solution Approach 2:
The server and database act as intermediary layers that validate and verify data from multiple communication interfaces before processing. This intermediary structure enables the system to handle diverse communication protocols and interfaces while maintaining security and detecting manipulations through centralized verification.
3Measurement precision
If multiple detection sets are used to cross-validate signals and identify anomaly sources, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the diagnostic system into multiple detection sets, each responsible for monitoring specific vehicle parameters or communication interfaces. This segmentation allows the system to divide complex monitoring tasks into manageable modules, improving measurement precision through specialized detection while organizing complexity into structured, independent units that can be processed separately.
Data Source
AI summary
Disclosed are systems and methods to detect and identify vehicular anomalies. Techniques to detect and identify the vehicular anomalies include receiving signals from various sensors, grouping the signals into detection sets, detecting anomalies by a comparison to vehicle behavior models, and cross-referencing the detection sets with each other to narrow down and identify the source of the anomaly. The detection sets may be grouped such that facets of vehicle maneuverability are captured and cover causal relations between different maneuverability mechanisms.