Image Extraction Method for Autonomous Driving SDL Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automatic drive car technologies face challenges such as high computational complexity, limited ability to handle multi-objective control, and ethical dilemmas like the Trolley problem, due to traditional neural network models' inefficiencies and lack of human-like judgment and sensory fusion capabilities.
Innovation Solution
An image extraction method is introduced that uses an SDL model with eigenvector generation and machine learning to improve image processing and recognition, enabling a more powerful machine learning model for automatic drive cars to learn from human drivers and achieve optimized control for safe driving, comfortable riding, and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional neural network models are used for automatic drive car control, then the system can process driving data, but the computational complexity becomes huge and hardware overhead increases
Solution Approach 1:
The patent segments the complex neural network model into multiple simpler sub-models or modules that can process different aspects of driving control independently. This segmentation reduces the computational burden on any single component while maintaining the overall automation capability of the system.
Solution Approach 2:
The patent extracts and removes unnecessary or redundant computational elements from the traditional neural network model, keeping only the essential components needed for automatic drive control. This extraction process reduces hardware overhead and computational complexity while preserving the core automation functionality.
2Adaptability or versatility
If traditional neural network models are used, then the system can learn from data, but it lacks human-like judgment and sensory fusion capabilities
Solution Approach 1:
The patent creates a composite model that integrates multiple types of algorithms and processing mechanisms, combining the strengths of different approaches to achieve both human-like judgment and reliable sensory fusion. This composite structure allows the system to process sensory data more effectively while maintaining adaptability.
3Measurement precision
If more training data is used to improve learning accuracy, then the model performance improves, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing and preprocessing of training data before the main training process, organizing and optimizing the data structure in advance. This preliminary action reduces the computational workload during actual training, allowing the model to achieve high recognition accuracy with reduced training time and resource consumption.
Data Source
AI summary
The invention refers to an image extraction method importing into SDL model in the field of information processing, and is characterized in that the target image are labeled artificially for plural times on computer data. The target image that has been labeled to be extracted for plural times will obtain the maximum probability value and scale of each parameter constituting the target image by machine learning. And the target image can be obtained from the sample computer data according to the maximum probability value or its scale range. The implementation effect of this method is to extract the required image arbitrarily from an image, eliminate the interference of background image affecting the result of image recognition, and improve the effect of image processing and the accuracy of image recognition, which is a new image processing algorithm that subverts the traditional binaryzation algorithm.