Autonomous Vehicle Image Merging for Low-Visibility Route Recognition
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Solution Overview
Problem
Self-driving vehicles struggle to accurately identify traveling routes in varying weather and time conditions, leading to frequent malfunctions and disabled self-driving functions.
Innovation Solution
A vehicle system that captures images of the surrounding environment and merges them with reference images having high visibility, using a processor to identify and replace corresponding traffic sign information, and update images based on visibility comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the self-driving vehicle uses image recognition to identify traveling routes, then the vehicle can autonomously navigate, but the recognition accuracy deteriorates under varying weather and time conditions
Solution Approach 1:
The system pre-stores reference images captured under various weather and time conditions in a database before actual self-driving operations. When the current image quality is insufficient, the system retrieves pre-stored reference images that match the current location and conditions, ensuring accurate traffic sign recognition without real-time capture limitations.
Solution Approach 2:
The system creates and uses copy images by merging current low-quality images with pre-stored high-quality reference images. This copying approach allows the system to generate enhanced images that retain accurate traffic sign information from reference images while maintaining the contextual accuracy of the current environment.
2Adaptability or versatility
If the vehicle captures and processes real-time images of the surrounding environment, then the vehicle can adapt to current conditions, but the visibility of traffic sign information deteriorates under poor weather and lighting conditions
Solution Approach 1:
The system merges the current captured image with a pre-stored reference image that corresponds to the same location. This combination allows the system to maintain adaptability to current environmental conditions while compensating for poor visibility by overlaying or blending with the higher-visibility reference image, ensuring traffic signs remain clearly visible.
Solution Approach 2:
The processor acts as an intermediary that compares current image quality with pre-stored reference images and selectively combines them. When visibility is insufficient, the processor mediates between the current low-visibility image and the pre-stored high-visibility reference image to generate an enhanced output image with improved traffic sign visibility.
3Measurement precision
If the vehicle updates reference images frequently to maintain accuracy, then the recognition precision improves, but the energy consumption and processing time increase
Solution Approach 1:
The system performs image capture and processing in advance, storing high-quality reference images in a database before they are needed for self-driving operations. This preliminary action eliminates the need for frequent real-time image processing during actual driving, significantly reducing energy consumption while maintaining high recognition precision through pre-processed accurate images.
Solution Approach 2:
The system processes and stores more reference images than immediately necessary, creating an extensive pre-stored database covering various conditions. This excessive preliminary processing ensures that when self-driving begins, the system can quickly retrieve appropriate reference images without needing to process additional images in real-time, balancing precision requirements with energy efficiency.
Data Source
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Figure 3A(a)~3A(c)
AI summary
Disclosed is a vehicle. The vehicle comprises: storage in which a plurality of reference images and position information corresponding to respective plurality of reference images are stored; and a processor which acquires through a camera an image of the surrounding environment in which the vehicle is traveling, acquires a reference image corresponding to the current position of the vehicle among the plurality of reference images if the visibility of the surrounding environment image satisfies a predetermined condition, acquires an image in which the surrounding environment image and the reference image are merged, and controls the operation of the vehicle on the basis of the merged image.