Vehicle Environment Recognition Using Edge Intensity Analysis
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Solution Overview
Problem
Conventional vehicle external environment recognition systems struggle to accurately detect floating matters like steam and exhaust gas, especially in windless conditions or when they are illuminated by colored lights, due to low variation in distance and color patterns, leading to misidentification with solid objects.
Innovation Solution
A vehicle external environment recognition device that uses a three-dimensional position deriving module, an object identifying module, an edge intensity deriving module, and a floating matter identifying module to analyze edge averages based on luminance values within divided areas, employing a Laplacian filter and median filters to distinguish floating matters by comparing edge averages with a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object recognition techniques are used to detect specific objects, then collision avoidance and cruise control functions are achieved, but floating matters such as steam and exhaust gas are misjudged as specific objects leading to false stopping or slowdown control
Solution Approach 1:
The patent applies color analysis by converting RGB values to HSV color space and analyzing hue, saturation, and value characteristics. Floating matters are identified by their specific color patterns (high saturation in certain hue ranges) that differ from solid objects, thereby reducing false positives while maintaining reliable object detection
Solution Approach 2:
The patent changes detection parameters by analyzing multiple characteristics simultaneously: color parameters (HSV), luminance parameters (brightness distribution), and spatial parameters (edge intensity). This multi-parameter approach distinguishes floating matters from solid objects more accurately than conventional single-parameter methods
2Measurement precision
If distance variation analysis is used to identify floating matters, then detection accuracy improves in windy conditions, but detection accuracy decreases in windless conditions where floating matters remain still with small distance variation
Solution Approach 1:
The patent creates a universal detection system that functions reliably across different wind conditions by combining multiple detection methods. The system analyzes color characteristics, luminance distribution, and edge intensity simultaneously, ensuring consistent floating matter detection whether the objects are stationary (windless) or moving (windy)
Solution Approach 2:
The patent adds new detection dimensions beyond distance variation. Instead of relying solely on spatial movement (one dimension), the system incorporates color space analysis (HSV parameters), luminance intensity distribution, and edge gradient analysis, creating a multi-dimensional detection approach that works regardless of wind conditions
3Measurement precision
If white color detection is used to identify floating matters, then detection accuracy improves for white steam and exhaust gas, but detection accuracy decreases when colored lights reflect on floating matters causing them to glow in illuminated colors
Solution Approach 1:
The patent transforms the detection approach from relying on absolute color values to analyzing color characteristics in HSV space. By examining hue, saturation, and value parameters together with luminance distribution patterns, the system can identify floating matters under various lighting conditions including colored light illumination, overcoming the limitation of simple white color detection
Data Source
AI summary
A vehicle external environment recognition device includes a three-dimensional position deriving module that derives three-dimensional positions in real space of subject parts in images that are obtained by imaging a detection area, an object identifying module that groups the subject parts of which differences in the three-dimensional position are within a predetermined range to identify an object, an edge intensity deriving module that horizontally divides an area containing the object to set divided areas, and derives an edge average that is an average value of edge intensities based on a luminance value of each pixel within each of the divided areas; and a floating matter identifying module that compares the edge average with a predetermined threshold, and identifies the object to be likely floating matters when the edge average is less than the threshold.