Vehicle Sensor Anomaly Detection for Faulty Signal Identification
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Solution Overview
Problem
Vehicles, particularly unmanned aerial vehicles (UAVs), face operational errors due to inaccurate or missing sensor signals, which can result from sensor failures or environmental conditions, leading to potential safety hazards if not identified and addressed.
Innovation Solution
A system and method for identifying anomalous sensors using a sensor anomaly detector with an anomalous sensor model trained via machine learning, which monitors sensor signals, determines inconsistencies, and generates a confidence level to identify sensors generating inaccurate data, allowing for real-time detection and potential recalibration or fault signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors are used to generate sensor signals for vehicle control and navigation, then the vehicle can operate autonomously, but sensor failures or environmental conditions may cause inaccurate sensor signals leading to control errors
Solution Approach 1:
The system continuously monitors sensor signals and compares them against expected ranges and correlations. When a sensor signal deviates from expected values or shows inconsistent patterns compared to other sensors, the system detects and flags the anomaly, providing feedback to identify and correct sensor failures before they compromise vehicle control
Solution Approach 2:
The patent introduces an intermediary monitoring system that acts as a mediator between the sensors and the vehicle control system. This intermediary layer analyzes sensor signal consistency and correlates data from multiple sensors to identify anomalies, preventing faulty sensor data from directly affecting vehicle control decisions
2Measurement precision
If multiple sensors are deployed to improve measurement accuracy and redundancy, then navigation precision increases, but the complexity of identifying faulty sensors among them increases
Solution Approach 1:
The system segments the sensor monitoring task by evaluating each sensor's signal independently against expected ranges and patterns, then comparing correlations between different sensors. This segmentation allows the system to identify which specific sensor is anomalous without requiring complex analysis of the entire sensor system simultaneously
Solution Approach 2:
The patent applies a universal monitoring approach that uses the same anomaly detection methods across all sensors regardless of type. The system correlates data from multiple sensor sources using consistent criteria, allowing the same identification process to work for various sensor types and reducing overall system complexity
Data Source
AI summary
A sensor system may include first and second sensors configured to be coupled to a vehicle and generate respective first and second sensor signals indicative of operation of the vehicle. The sensor system may also include a sensor anomaly detector including an anomalous sensor model configured to receive the first and second sensor signals and determine that one or more of the first sensor or the second sensor is an anomalous sensor generating inaccurate sensor data. The sensor system may also be configured to identify one or more of the first sensor or the second sensor as the anomalous sensor generating inaccurate sensor data.