Autonomous Sensor Auto-Checking for Real-Time Camera Fault Correction
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Solution Overview
Problem
Conventional techniques for autonomous machines are unable to effectively detect and correct low-quality or misleading data from sensors, such as cameras, which can compromise the accuracy of autonomous systems, especially in critical situations like autonomous driving.
Innovation Solution
A deep learning-based sensor auto-checking mechanism that uses convolutional neural networks to continuously monitor sensors for abnormalities, such as obstructions or technical issues, and provides real-time detection and correction, ensuring high-quality data input.
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 correcting low-quality or misleading data from compromised sensors
Solution Approach 1:
The patent implements a feedback mechanism where the autonomous machine's own sensor data is used to monitor and detect compromised sensors. The system continuously compares sensor readings with expected patterns and environmental context, automatically identifying when sensors are providing low-quality or misleading data, and can correct or compensate for these issues in real-time.
Solution Approach 2:
The autonomous machine performs self-diagnosis of its sensor system using deep learning models that analyze sensor data quality. The system automatically detects compromised sensors, identifies the nature of the compromise (obstruction, technical defect, spoofing), and applies corrections without external intervention, enabling the system to serve its own quality assurance needs.
2Reliability
If conventional sensor fusion techniques are used, then redundancy is provided, but the system cannot detect or correct compromised sensors in real-time
Solution Approach 1:
The patent employs deep learning models that are pre-trained to recognize patterns of sensor compromise before they affect autonomous operation. The system continuously monitors sensor data quality metrics and detects anomalies in real-time, enabling early warning and immediate correction of compromised sensors before they can jeopardize safety-critical decisions.
Solution Approach 2:
The system implements continuous real-time monitoring of all sensor inputs using automated quality assessment algorithms. This ongoing detection and correction process ensures that sensor data quality is maintained throughout autonomous operation, with the system continuously adapting to identify and correct compromised sensors without interruption to the autonomous function.
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.