Vehicle Surround Sensing Using Wheel and Front-Rear Image Coordinates
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Solution Overview
Problem
Current autonomous driving technologies face challenges in accurately and efficiently calculating the distance and direction of surrounding vehicles using image recognition, as existing methods often result in erroneous detections and fail to effectively utilize the positional relationships between entire vehicle images, wheel images, and front-rear images.
Innovation Solution
A vehicle system equipped with a camera and controller that acquires entire images of surrounding vehicles, derives coordinates of wheel and front-rear image areas, and determines distance and direction information based on relative positional relationships, including grouping images and identifying erroneous detections through coordinate comparisons and intersection calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image recognition is used to detect surrounding vehicles, then the system can acquire information about nearby vehicles, but erroneous detections occur and distance/direction calculation accuracy deteriorates
Solution Approach 1:
The patent segments the vehicle image into multiple specific regions including wheel regions, front-rear regions, and body regions. By detecting these segmented regions separately and analyzing their coordinate relationships, the system achieves more accurate distance and direction calculations while reducing erroneous detections compared to treating the entire vehicle image as a single object.
2Measurement precision
If only entire vehicle images are used for detection, then the system complexity is low, but the ability to accurately determine distance and direction information is insufficient
Solution Approach 1:
The patent divides the vehicle image into specific regions (wheel regions, front-rear regions, body regions) and derives coordinates from these segmented areas. This segmentation enables accurate distance and direction determination by analyzing the spatial relationships between different region coordinates, achieving high measurement precision while maintaining reasonable processing complexity.
Solution Approach 2:
The patent transitions from using only entire vehicle image coordinates to utilizing multi-dimensional coordinate information from segmented regions. By deriving coordinates from wheel regions, front-rear regions, and analyzing their relative positional relationships in multiple dimensions, the system achieves accurate 3D distance and direction information from 2D images.
3Measurement precision
If coordinate information from multiple image areas is used, then distance calculation accuracy improves, but the complexity of coordinate derivation and validation increases
Solution Approach 1:
The patent segments the vehicle image into wheel regions, front-rear regions, and body regions, deriving coordinates from each segment. This segmentation approach improves distance calculation precision by utilizing the spatial relationships between different region coordinates while organizing the coordinate processing in a structured manner that manages complexity.
Solution Approach 2:
The patent implements validation mechanisms that check whether derived coordinates meet expected spatial relationships (e.g., verifying that front-rear region coordinates are positioned appropriately relative to wheel region coordinates). This feedback approach ensures calculation accuracy while filtering out erroneous detections, managing the complexity of processing multiple coordinate sets.
Data Source
AI summary
An apparatus for acquiring surrounding information of a vehicle includes: a camera configured to acquire an entire image of at least one surrounding vehicle; and a controller configured to derive at least one of coordinates of a wheel image area or coordinates of a front-rear image area included in an entire image area, and determine distance information from the vehicle to the at least one surrounding vehicle based on a relative positional relationship between the entire image area and the at least one of the wheel image area coordinates or the front-rear image area coordinates.