Vehicle Vision System Retroreflector Pattern Recognition
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Solution Overview
Problem
Conventional vehicle vision systems fail to effectively identify and classify passive light sources, such as reflections from retroreflective reflectors, which are crucial for enhancing night driving safety and road obstacle detection, especially under low light conditions.
Innovation Solution
A vehicle vision system utilizing one or more CMOS cameras and an image processor that captures and processes image data to identify and classify retroreflective reflectors by comparing their patterns and movements with a database, allowing for the detection of objects like bicycles, pedestrians, and road markings even in low light environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vehicle vision systems are used, then the system structure is simple, but the ability to identify passive light sources in low light conditions deteriorates
Solution Approach 1:
The system segments the identification process into distinct stages: capturing image data from multiple cameras, processing the data through an image processor, comparing patterns against a database, and classifying objects. This segmentation allows each component to be optimized for its specific function while maintaining overall system reliability for identifying passive light sources in low light conditions.
2Measurement precision
If pattern recognition is used to identify retroreflective reflectors, then the detection precision of road obstacles improves, but the complexity of data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-storing retroreflector patterns and their associated object classifications in a database before actual detection occurs. During operation, the image processor simply compares captured patterns against this pre-established database, significantly reducing real-time processing complexity while maintaining high detection precision for road obstacles.
3Area of stationary object
If multiple cameras are used to capture image data, then the coverage area increases, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the relevant features from the large volume of image data captured by multiple cameras - specifically focusing on identifying retroreflector patterns and their movements. By extracting only these critical elements for comparison against the database, the system maintains comprehensive field of view coverage while reducing the effective data volume that requires intensive processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances night driving safety by accurately identifying and classifying passive light sources, improving road obstacle detection and path prediction, thereby assisting drivers in navigating through low-light conditions.
Implementation Method 1
identification of passive light sources, such as reflections of light... classify passive light sources or retroreflective reflectors present in the field of view
Data Source
AI summary
A vision system of a vehicle includes a camera and an image processor. The camera is configured to be disposed at a vehicle so as to have a field of view exterior of the vehicle. The image processor is operable to process image data captured by the camera to classify patterns of retroreflective reflectors present in the field of view of the camera. The image processor compares determined patterns of retroreflective reflectors to a database of patterns and classifies patterns of retroreflective reflectors at least in part responsive to determination that determined patterns of retroreflective reflectors generally match a pattern of the database. The image processor may compare movement of determined patterns of retroreflective reflectors and pattern movements of the database over multiple frames of captured image data and may classify the retroreflective reflectors at least in part responsive to determination that the movements generally match.