Trailer Vision Alignment Using CNN-Based Detection and Classification
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Solution Overview
Problem
Existing vehicle vision systems struggle to accurately detect and classify trailers of various types, orientations, and environmental conditions, which hinders autonomous alignment and navigation of vehicles with trailers.
Innovation Solution
A vehicle vision system utilizing one or more cameras and a deep neural network (DNN) to process image data, detect the presence of trailers, determine their position, and classify them into different categories, enabling autonomous alignment and navigation of vehicles with trailers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to detect trailers, then the system complexity is low, but the detection accuracy and classification precision are insufficient
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a deep neural network (DNN)-based vision system. The DNN automatically learns and extracts features from images to detect and classify trailers, substituting manual feature engineering and traditional algorithms with an intelligent system that achieves superior detection accuracy and classification precision while handling various trailer types, orientations, and environmental conditions.
2Reliability
If multiple cameras and advanced processing are deployed to handle various trailer conditions, then the detection reliability improves, but the device complexity increases
Solution Approach 1:
The patent implements a universal DNN-based vision system that handles multiple functions including detection, classification, and alignment for various trailer types, orientations, and environmental conditions through a single integrated system. The DNN is trained to recognize diverse trailer scenarios, making the system adaptable and reliable across different situations without requiring separate specialized systems for each condition.
Solution Approach 2:
The patent replaces complex mechanical and manual processing systems with an intelligent DNN-based vision system that automatically adapts to various trailer conditions. The DNN learns from training data to handle diverse scenarios, reducing the need for multiple specialized components while maintaining high detection reliability across different environments and trailer configurations.
3Ease of operation
If autonomous alignment and navigation are implemented, then the ease of operation improves, but the extent of automation requires sophisticated systems that may increase complexity
Solution Approach 1:
The patent implements a self-service autonomous alignment and navigation system where the DNN-based vision system automatically detects trailers, determines their positions and orientations, and guides the vehicle without requiring manual intervention. The system performs self-alignment by processing images and calculating the necessary steering adjustments, enabling the vehicle to autonomously back into the trailer hitch, thereby significantly improving ease of operation.
Solution Approach 2:
The patent replaces manual alignment and navigation operations with an automated DNN-based vision system. The system uses deep learning to interpret camera images, determine trailer positions and orientations, and automatically control vehicle steering and movement, substituting complex manual coordination with intelligent automation that simplifies the hitching process for the user.
Data Source
AI summary
A vehicular trailer assist system includes a camera disposed at a rear portion of a vehicle and viewing at least rearward of the vehicle, and an electronic control unit (ECU), which includes an image processor for processing image data captured by the camera. The ECU executes a deep convolutional neural network (CNN) to determine, via processing of image data captured by the camera, presence of a trailer rearward of the vehicle. The CNN, responsive to processing of image data captured by the camera, determines a position of a trailer relative to the vehicle. The CNN, responsive to determining the position of the trailer, classifies the trailer into a trailer category. The ECU, responsive to the CNN determining the position of the trailer, generates an output based on the trailer classification to autonomously control the vehicle to align the vehicle with the trailer and navigate the vehicle toward the trailer.