Grid-Generated CNN Parameter Mapping for Varied Road Layouts
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Solution Overview
Problem
Convolutional Neural Networks (CNN) used in autonomous driving struggle with inefficiency when input images deviate from the typical arrangements used in training images, as parameters optimized for central roads may not properly process test images with differently arranged road features.
Innovation Solution
A method utilizing a grid generator to divide test images into subsections based on dynamic templates, incorporating non-object location and class information to determine optimized neural network parameters for testing, allowing for effective processing of varied road arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CNN parameters are optimized for training images with typical road arrangements (roads in centers), then processing efficiency is improved for those specific arrangements, but processing accuracy deteriorates when test images have different road arrangements
Solution Approach 1:
The patent divides the input image into multiple regions of interest (ROIs) based on detected non-object locations. Each ROI is then processed independently with appropriate CNN parameters selected based on the regional characteristics. This segmentation allows different parameter sets to be applied to different spatial locations, resolving the contradiction between optimized processing for typical arrangements and accurate processing for varied arrangements.
Solution Approach 2:
The patent dynamically selects CNN parameters based on the actual content and arrangement of objects in each region of the input image. Rather than using fixed parameters optimized for a single typical arrangement, the system adapts parameter selection to match the specific spatial configuration found in each test image, thereby maintaining both efficiency and accuracy across diverse scenarios.
2Device complexity
If CNN uses fixed parameters optimized for typical training image arrangements, then model simplicity is maintained, but adaptability to different road arrangements deteriorates
Solution Approach 1:
The patent performs preliminary detection of non-objects and division into regions of interest before applying CNN processing. This preliminary action identifies the spatial arrangement characteristics of the input image, enabling subsequent dynamic parameter selection. The preprocessing step adds minimal complexity while significantly improving adaptability to different road arrangements.
Solution Approach 2:
The patent applies different CNN parameters to different regions of the input image based on local characteristics. Each region receives parameter optimization tailored to its specific content and arrangement, rather than applying a single global parameter set. This local adaptation maintains model simplicity overall while achieving high adaptability to various road configurations.
Data Source
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AI summary
A method of neural network operations by using a grid generator is provided for converting modes according to classes of areas to satisfy level 4 of autonomous vehicles. The method includes steps of: (a) a computing device, if a test image is acquired, instructing a non-object detector to acquire non-object location information for testing and class information of the non-objects for testing by detecting the non-objects for testing on the test image; (b) the computing device instructing the grid generator to generate section information by referring to the non-object location information for testing; (c) the computing device instructing a neural network to determine parameters for testing; (d) the computing device instructing the neural network to apply the neural network operations to the test image by using each of the parameters for testing, to thereby generate one or more neural network outputs.