Dynamic Image Stitching for Vehicular Vision Systems
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Solution Overview
Problem
Existing vehicle vision systems face challenges in maintaining seamless image stitching due to changes in vehicle load or terrain, leading to misalignments and discontinuities in the stitched composite image displayed to the driver.
Innovation Solution
A dynamic image stitching system that monitors overlapping regions of adjacent camera fields of view and adjusts the stitching algorithm in real-time to adapt to changes in vehicle load, terrain, and road conditions, using image processing to ensure proper alignment and seamlessness of the stitched images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a plurality of cameras are used to provide a surround view, then the driver's field of view is improved, but image misalignment and stitching discontinuities occur when vehicle load or terrain changes
Solution Approach 1:
The patent implements dynamic calibration that automatically adjusts camera parameters in real-time based on detected vehicle load conditions and terrain changes. The system transitions from static factory calibration to dynamic adaptation, allowing the image stitching parameters to change continuously with vehicle operating conditions, thereby maintaining alignment precision across varying loads and terrains.
Solution Approach 2:
The system changes calibration parameters dynamically by detecting reference objects in overlapping camera fields and calculating adjusted stitching parameters based on detected positional discrepancies. This allows the system to adapt image alignment parameters to current vehicle load and terrain conditions without physical recalibration.
2Manufacturing precision
If factory calibration is used for image stitching, then initial alignment is achieved, but the system cannot adapt to changes in vehicle load or terrain
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors overlapping regions of adjacent camera fields, detects reference objects, and measures positional discrepancies. This feedback loop enables automatic recalibration of stitching parameters to maintain alignment precision under varying vehicle load and terrain conditions.
Solution Approach 2:
The system performs preliminary detection of reference objects in overlapping camera regions to determine current alignment status, then uses this information to proactively adjust stitching parameters before significant misalignment occurs. This preliminary action enables preventive calibration adjustments.
3Manufacturing precision
If manual recalibration is required for load changes, then alignment precision can be maintained, but system complexity and operation difficulty increase
Solution Approach 1:
The patent implements self-calibration functionality where the system automatically detects reference objects, calculates alignment discrepancies, and adjusts stitching parameters without user intervention. This eliminates the need for manual recalibration operations while maintaining high alignment precision across varying vehicle conditions.
Solution Approach 2:
The system uses automatically detected reference objects as intermediaries to transfer alignment information between adjacent camera fields. These reference objects serve as mediators that enable the system to self-correct stitching parameters without requiring manual measurement or user input.
Data Source
AI summary
A vehicular vision system includes a plurality of cameras disposed at a vehicle and capturing image data as the equipped vehicle is driven along a traffic lane of a road. Captured image data is processed to detect a rearward-approaching vehicle and determine a path of travel of the detected rearward-approaching vehicle. Responsive to determining that the path of travel of the detected rearward-approaching vehicle is along a side traffic lane adjacent to the traffic lane along which the equipped vehicle is driven, the vehicular vision system (i) displays at a video display screen video images derived at least from image data captured by the side camera that is at the side portion of the equipped vehicle that is adjacent to the side traffic lane and (ii) overlays a transparent graphic overlay overlaying the displayed video images that is based on the determined path of travel of the detected rearward-approaching vehicle.