Autonomous Sensor Auto-Checking for Real-Time Compromised Detection
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Solution Overview
Problem
Conventional techniques for autonomous machines are unable to effectively detect and address low-quality or misleading data from sensors, such as cameras, which can compromise the accuracy and reliability of autonomous systems, especially in critical applications like self-driving vehicles.
Innovation Solution
A deep learning-based approach that employs a sensor auto-checking mechanism to detect abnormalities in sensors, issue real-time alerts, and correct issues such as obstructions or technical defects using convolutional neural networks (CNNs) for continuous monitoring and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used for data fusion to provide redundancy, then reliability is improved, but the system becomes incapable of detecting or addressing low-quality or misleading data from compromised sensors
Solution Approach 1:
A deep learning-based intermediary system is introduced between the sensors and the autonomous machine's decision-making processes. This intermediary continuously monitors sensor data quality, detects compromised sensors through pattern recognition, and filters out misleading data before it reaches the control system, thereby maintaining both reliability and detection capability
Solution Approach 2:
The system implements continuous feedback loops where sensor outputs are constantly evaluated against expected patterns and physical constraints. When anomalies are detected, the system provides feedback to identify and isolate compromised sensors, allowing the data fusion process to adaptively weight or exclude unreliable sensor inputs while maintaining overall system reliability
2Reliability
If conventional sensor fusion techniques are used, then redundancy is provided, but the system cannot deal with sensors providing low quality or misleading data
Solution Approach 1:
The system dynamically adapts its sensor fusion strategy based on real-time sensor performance evaluation. When a sensor is identified as compromised, the system dynamically adjusts weighting factors, switches to alternative sensors, or modifies fusion algorithms to accommodate the degraded input, thereby maintaining accuracy while handling diverse sensor conditions
Solution Approach 2:
The system changes operational parameters such as sensor weighting coefficients, data fusion thresholds, and confidence levels based on the detected quality of each sensor. This allows the system to maintain high accuracy by adjusting parameters to compensate for compromised sensors while preserving the ability to handle various sensor failure modes
Data Source
AI summary
A mechanism is described for facilitating deep learning-based real-time detection and correction of compromised sensors in autonomous machines according to one embodiment. An apparatus of embodiments, as described herein, includes detection and capturing logic to facilitate one or more sensors to capture one or more images of a scene, where an image of the one or more images is determined to be unclear, where the one or more sensors include one or more cameras. The apparatus further comprises classification and prediction logic to facilitate a deep learning model to identify, in real-time, a sensor associated with the image.