Deep Neural Network for Sensor Blindness Detection in Autonomous Driving
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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 that uses region and context-based detection techniques to classify sensor blindness regions in images, allowing for real-time detection and classification of sensor data usability, reducing computational requirements and enabling adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature-level computer vision techniques are used to detect sensor blindness, then detection capability is achieved, but computational cost becomes too high for real-time deployment
Solution Approach 1:
The patent extracts only the most critical features needed for sensor blindness detection rather than analyzing all image features. By selectively extracting key visual evidence of sensor blindness conditions, the system achieves accurate detection while minimizing computational overhead, enabling real-time processing.
Solution Approach 2:
The patent segments the image analysis process into distinct regions and feature types, analyzing only relevant portions for sensor blindness detection. This segmentation allows the system to focus computational resources on critical areas, reducing overall processing time while maintaining detection accuracy.
2Measurement precision
If conventional computer vision techniques analyze multiple features separately, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple feature analysis processes into a unified detection framework. By combining edge feature analysis, color-based pixel analysis, and other feature detection methods into a single integrated system, the patent reduces complexity while maintaining the ability to detect various sensor blindness conditions accurately.
Solution Approach 2:
The patent creates a universal detection system that handles multiple types of sensor blindness conditions (occlusion, blur, glare, etc.) through a single multi-functional algorithm. This universal approach eliminates the need for separate specialized analyses for each condition, reducing system complexity while preserving detection accuracy.
3Reliability
If hard-coded computer vision techniques are used, then initial detection performance is achieved, but adaptability to new conditions is lost
Solution Approach 1:
The patent implements a dynamic detection system that can adapt to new sensor blindness conditions through machine learning. The system continuously learns from new data and adjusts its detection parameters, transitioning from static hard-coded rules to dynamic adaptive algorithms that improve performance over time while maintaining reliability.
Solution Approach 2:
The patent incorporates feedback mechanisms where detection results are used to refine and improve the detection algorithm. By analyzing detection outcomes and using this feedback to adjust parameters and learn from new conditions, the system maintains high reliability while gaining adaptability to previously unseen sensor blindness scenarios.
Data Source
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.