Automotive Camera Visibility Assessment via Dynamic Blindness Detection
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Solution Overview
Problem
Existing methods for determining camera visibility in automotive surroundings, such as fog detection algorithms and 3D unit-based methods, are inadequate in accurately assessing blindness and its impact on camera functions, particularly under varying illumination conditions and when condensation occurs.
Innovation Solution
A method that divides the camera image into partial images, calculates visibility and blindness probabilities, and generates blindness signals, with sensitivity dependent on illumination, allowing for debouncing based on detection time and specific threshold adjustments for different brightness levels, thereby enhancing the accuracy of blindness detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera image analysis is performed using traditional fog detection algorithms or 3D unit-based methods, then visibility can be assessed to some extent, but accuracy is insufficient particularly under varying illumination conditions and when condensation occurs
Solution Approach 1:
The patent applies parameter changes by adjusting the sensitivity of blindness detection based on illumination conditions. The control unit modifies detection parameters dynamically - when illumination is low, the system reduces sensitivity to avoid false blindness detections, while under normal illumination, full sensitivity is applied. This resolves the contradiction by making the measurement system adaptable to varying environmental parameters rather than using fixed thresholds.
Solution Approach 2:
The patent divides the camera image into multiple regions of interest (ROIs) and performs separate blindness detection for each region. This local quality approach allows different parts of the image to be evaluated with region-specific parameters and thresholds, improving overall detection accuracy while accounting for local variations in illumination and condensation patterns that global methods would miss.
2Measurement precision
If blindness detection sensitivity is increased to detect all potential camera issues, then detection accuracy improves, but false detections increase particularly in low illumination conditions
Solution Approach 1:
The patent implements dynamic sensitivity adjustment where the blindness detection threshold is not fixed but adapts based on current illumination conditions. The control unit continuously monitors illumination levels and dynamically modifies the detection sensitivity - reducing it during nighttime or low-light conditions to prevent false positives, while maintaining high sensitivity during daytime. This dynamic approach resolves the contradiction between detection accuracy and false alarm reduction.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results from previous time periods influence current detection parameters. The control unit uses historical data about illumination patterns and detection outcomes to continuously refine detection thresholds, creating a feedback loop that reduces false detections while maintaining high accuracy. This adaptive feedback system allows the patent to balance sensitivity and reliability.
3Measurement precision
If the entire camera image is analyzed for blindness detection, then comprehensive coverage is achieved, but processing time and computational load increase
Solution Approach 1:
The patent segments the camera image into multiple regions of interest (ROIs) corresponding to different functional areas (e.g., lane detection zone, pedestrian detection zone). Instead of analyzing the entire image uniformly, the system performs blindness detection only in these specific regions using parallel processing. This segmentation approach maintains comprehensive coverage of all critical areas while significantly reducing overall processing time through divided computational tasks.
Solution Approach 2:
The patent applies partial action by focusing blindness detection only on critical regions of the image rather than analyzing every pixel uniformly. The control unit identifies and prioritizes regions where blindness would most impact safety functions, applying full detection rigor to these areas while using reduced processing for less critical regions. This selective approach achieves sufficient comprehensiveness with reduced computational overhead.
Data Source
AI summary
A method for checking the visibility of a camera for surroundings of an automobile is proposed which includes receiving a camera image and a step of dividing the camera image into a plurality of partial images. A visibility value is determined based on a number of objects detected in the particular partial image. A visibility probability is subsequently determined for each of the partial images based on the blindness values and the visibility values of the particular partial images.