Rear Vehicle Light Detection via Brightness Segmentation
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Solution Overview
Problem
Modern driver assistance systems in vehicles face challenges in detecting and processing information about rear vehicle lights, including flashing lights or brake lights, which are essential for reacting to traffic situations, but existing methods require significant data processing and effort.
Innovation Solution
A method and device using a camera system to capture images of the area ahead, segmenting the image into sub-areas based on brightness values, extracting the brightest segments, and checking for specific lighting patterns to determine the state of rear vehicle lights, allowing for efficient detection and signaling of switched-on lights without processing large amounts of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver assistance systems process comprehensive image data to detect rear vehicle lights, then detection reliability is improved, but data processing complexity and computational effort increase significantly
Solution Approach 1:
The image processing is divided into sequential stages: initial image acquisition, region of interest identification, sub-area division into left/right sections, and further segmentation into image segments based on brightness thresholds. This hierarchical segmentation reduces the total data volume requiring complex processing while maintaining detection reliability by systematically analyzing relevant portions of the image.
Solution Approach 2:
The method extracts only the brightest image segments from each sub-area, isolating the most relevant information (potential light sources) while discarding redundant data. This extraction principle reduces processing complexity by focusing computational resources on critical regions rather than analyzing the entire image comprehensively.
2Productivity
If the system processes minimal data to reduce computational effort, then processing speed is improved, but detection precision may deteriorate
Solution Approach 1:
Different processing strategies are applied to different regions of the image. The image area is divided into sub-areas (left and right) corresponding to different vehicle lighting systems, with each sub-area processed independently. This local quality approach maintains detection precision by preserving spatial information about which lighting system is active, while improving processing speed through region-specific analysis.
Solution Approach 2:
The system performs partial action by processing only the brightest image segments rather than analyzing all pixels in the image. This selective processing maintains sufficient detection precision for identifying active lighting systems while dramatically improving processing speed by avoiding exhaustive analysis of the entire image data set.
3Measurement precision
If the system divides the image area into multiple sub-areas and segments for detailed analysis, then detection accuracy is improved, but the amount of data to be processed increases
Solution Approach 1:
The image area is pre-divided into sub-areas (left and right) and further segmented into brightness-based image segments before detailed analysis. This preliminary action organizes the data structure in advance, enabling efficient processing by knowing exactly which segments to analyze and in what order, thereby reducing the effective data volume requiring detailed processing while maintaining detection accuracy.
Data Source
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AI summary
The invention relates to a method for detecting a state of a rear vehicle light. In said method, a camera system integrated in a vehicle (1) generates an image of the surroundings in front of the vehicle (1), wherein an evaluation unit (8) connected to the camera system detects at least one vehicle (10) contained in the image, defines an image range (11) associated with said vehicle (10) and divides the image range (11) into sub-ranges (12, 13, 14) comprising at least a left sub-range (12) and a right sub-range (13). A segmentation of the sub-ranges (12, 13, 14) into image segments is performed by way of the evaluation unit (8) on the basis of brightness values of points contained in said sub-ranges (12, 13, 14), wherein at least one brightest image segment is extracted from each of said sub-ranges (12, 13, 14), wherein the extracted image segments are checked for whether they can be associated with a particular vehicle light in an ON state, and wherein the evaluation unit (8) emits a signal associated with the state thus determined, in the event said check turned out to be positive. The invention further relates to a device suitable for carrying out said method.