Trailer Recognition via Image Feature Extraction
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Solution Overview
Problem
Existing methods for automatically detecting a trailer attached to a towing vehicle at engine start are inadequate, as they fail to determine whether a trailer is present and unknown information about the trailer, such as drawbar length and articulation angle, is not available at engine startup.
Innovation Solution
A method and device that capture images of the trailer using a vehicle-mounted camera, extract features, and compare them to stored patterns to recognize known trailers, determining the trailer's presence, articulation angle, and associated information, which can be used immediately after engine start without driver input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic detection methods are used to identify trailers, then the need for manual driver input is reduced, but the system cannot determine trailer presence or obtain trailer information (such as drawbar length and articulation angle) at engine startup
Solution Approach 1:
The system performs preliminary actions by capturing images of the trailer and extracting其特征 during the engine startup phase before the vehicle begins operation. The control unit processes these images to identify trailer presence and extract relevant information such as drawbar length and articulation angle in advance, making this information available immediately when needed for safe vehicle operation.
2Measurement precision
If image processing is used to extract trailer features, then trailer recognition accuracy is improved, but the system must handle varying perspectives and self-concealment of features
Solution Approach 1:
The system addresses varying perspectives by capturing images from multiple dimensions or angles. The control unit processes these multi-dimensional images to extract trailer features that remain identifiable despite changes in viewing angle, thereby improving recognition accuracy while managing the complexity through systematic multi-angle analysis.
Solution Approach 2:
The system creates multiple copies or representations of trailer features from different image captures. By having multiple feature representations from various perspectives, the system can identify and match features even when some are concealed, improving recognition accuracy without requiring overly complex single-image processing.
3Reliability
If the system captures and processes multiple images to store trailer patterns, then recognition reliability is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary image capture and pattern storage during the engine startup phase and during maneuvers when the trailer is visible. By preparing and storing trailer patterns in advance during these natural operational windows, the system builds a reliable pattern library without adding significant time to the main vehicle operations.
Solution Approach 2:
The system performs periodic image capture and pattern updating during specific operational conditions such as engine startup and during vehicle maneuvers. This periodic approach allows the system to accumulate reliable trailer pattern data over time through multiple captures, improving recognition reliability while distributing the processing load across different operational periods rather than concentrating it all at once.
Data Source
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AI summary
In order to identify a trailer (2) hitched to a vehicle (10) the following steps are carried out: capturing an image (1) that shows at least part of the trailer (2); extracting at least one feature (6) of the trailer (2) from the image (1); establishing that the trailer (2) is a known trailer if the at least one feature (6) corresponds to a stored sample of the known trailer.