Deep Neural Network for Sensor Blindness Detection
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Solution Overview
Problem
Conventional systems for detecting sensor blindness in autonomous driving systems rely on feature-level computer vision techniques, which are computationally expensive and ineffective for real-time deployment, unable to differentiate between types of sensor blindness, and lack adaptability to new conditions.
Innovation Solution
A deep neural network processing system using region and context-based detection techniques to classify sensor blindness regions in images, reducing computational requirements and enabling real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feature-level computer vision techniques are used to detect sensor blindness, then individual visual evidence features can be analyzed, but computational expense increases and real-time deployment becomes ineffective
Solution Approach 1:
The patent segments the sensor blindness detection task into distinct blindness types (occlusion, blur, glare) and processes them through specialized neural network branches. This segmentation allows parallel processing of different blindness indicators, reducing overall computational expense while maintaining detection accuracy for each specific blindness type.
Solution Approach 2:
The patent replaces conventional feature-level computer vision techniques with a deep neural network-based system. This substitution transitions from manual feature analysis to automated learned feature extraction, significantly reducing computational expense while enabling real-time deployment. The neural network learns to identify blindness patterns directly from raw sensor data without requiring separate analysis of each visual feature.
2Adaptability or versatility
If conventional computer vision techniques analyze each feature separately to detect sensor blindness, then individual blindness indicators can be identified, but system complexity increases and scalability is limited
Solution Approach 1:
The patent implements a universal neural network architecture that handles multiple blindness types (occlusion, blur, glare) through a single integrated system. The network uses shared feature extraction layers followed by specialized classification branches, allowing it to adapt to different blindness conditions without requiring separate analysis systems for each type. This multi-functional approach simplifies the overall system while maintaining versatility.
Solution Approach 2:
The patent employs dynamic feature extraction where the neural network adapts its analysis based on the input characteristics. The system dynamically adjusts which features to emphasize depending on the detected blindness type, rather than uniformly analyzing all features for all conditions. This dynamic approach reduces complexity by focusing computational resources on relevant features for each specific blindness scenario.
3Adaptability or versatility
If conventional hard-coded computer vision techniques are used, then specific blindness detection rules can be implemented, but the system cannot learn from historical data or adapt to new conditions
Solution Approach 1:
The patent incorporates feedback mechanisms where the neural network learns from historical sensor data and detection outcomes. The system continuously refines its blindness detection capabilities by processing accumulated data, allowing it to adapt to new blindness conditions and improve performance over time. This feedback loop enables the system to learn patterns that were not explicitly programmed, capturing information from historical operations.
Solution Approach 2:
The patent performs preliminary training of the neural network using historical sensor data and labeled blindness examples before deployment. This preliminary action allows the system to pre-learn common blindness patterns and establish baseline detection capabilities. The pre-trained network can then quickly adapt to new conditions during operation, utilizing the knowledge gained from historical data without requiring complex real-time learning algorithms.
Data Source
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AI summary
In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations – such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data – or to filter out portions of instances of the sensor data determined to be compromised – that may lead to inaccurate or ineffective results for the one or more operations of the system.