Vehicle Camera Disparity Analysis for Light Interference Detection
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) and autonomous vehicles face challenges in accurately recognizing road features and obstacles due to light interference from direct or reflected bright light sources, which can cause image recognition failures and reduce safety.
Innovation Solution
A processor-based system that generates disparity images from camera data to detect light interference by comparing standard images with disparity images, triggering actions such as notifications or control adjustments based on the level of autonomy and lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cameras are used to capture visual representations for navigation and obstacle detection, then the vehicle's ability to recognize road features and obstacles is improved, but light interference from bright light sources can cause image blindness and make image recognition unreliable
Solution Approach 1:
The patent introduces an intermediary processing system that compares the original camera image with a disparity image (generated from stereo vision or sequential frames) to detect regions affected by light interference. This intermediary comparison mechanism identifies bright spots and light interference patterns that would otherwise blind the primary image recognition system, allowing the vehicle to compensate for these harmful effects.
2Reliability
If multiple sensor types are employed to enhance vehicle control and safety, then the system's ability to detect and avoid obstacles is improved, but the complexity of the system increases
Solution Approach 1:
The patent makes the existing camera system multi-functional by enabling it to perform both its primary function (capturing images for obstacle detection) and a secondary function (detecting light interference through disparity analysis). Instead of adding separate dedicated sensors solely for light detection, the system reuses the camera's existing optical path and processing pipeline, allowing one component to serve multiple purposes and reducing overall system complexity.
3Reliability
If the processor generates and compares disparity images to detect light interference regions, then the reliability of image recognition in adverse lighting conditions is improved, but the computational processing requirements increase
Solution Approach 1:
The patent applies partial action by not processing the entire image at full resolution for disparity comparison. Instead, it identifies and focuses computational resources on specific regions of interest where light interference is detected (bright spots), performing detailed disparity analysis only in those localized areas rather than across the whole image, thereby reducing overall computational load while maintaining detection reliability.
Data Source
AI summary
In some examples, a processor may receive images from a camera mounted on a vehicle. The processor may generate a disparity image based on features in at least one of the images. In addition, the processor may determine at least one region in a first image of the received images that has a brightness that exceeds a brightness threshold. Further, the processor may determine at least one region in the disparity image having a level of disparity information below a disparity information threshold. The processor may determine a region of light interference based on an overlap between at least one region in the first image and at least one region in the disparity image, and may perform at least one action based on the region of light interference.