Low-Dimensional ANN for Delimited Region Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing environment sensing technologies for driver assistance and automated driving face inefficiencies in determining delimited regions and motion paths, particularly due to high computational and memory requirements in semantic segmentation methods, which are not fully necessary for managing driving tasks.
Innovation Solution
An apparatus utilizing an artificial neural network (ANN) with a first layer receiving image input and a second layer outputting boundary lines or motion paths, where the dimensionality of the second layer is significantly lower than the first, allowing for a compact representation of delimited regions and motion paths as parameterized lines, reducing computational and memory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic segmentation methods are used to determine delimited regions and motion paths, then measurement precision and reliability are improved, but computational load and memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for driving tasks from the full image data. Instead of performing complete semantic segmentation that classifies every pixel, the system extracts only boundary lines of delimited regions and motion paths of objects. This extraction approach maintains sufficient detection precision for safety-critical applications while dramatically reducing computational load and memory requirements by eliminating unnecessary classification details.
Solution Approach 2:
The patent segments the problem of environment perception into two distinct components: (1) detection of boundary lines defining delimited regions, and (2) detection of motion paths of objects. This segmentation allows the system to focus computational resources on extracting only the geometrically essential features needed for navigation and collision avoidance, rather than performing exhaustive pixel-level semantic classification.
2Measurement precision
If complete semantic segmentation is performed on image data, then detection precision is improved, but processing time and energy consumption increase
Solution Approach 1:
The system extracts only the critical geometric features (boundary lines and motion paths) from image data, bypassing the time-consuming process of complete semantic segmentation. By directly identifying and extracting these essential elements using optimized algorithms, the system achieves sufficient detection precision for real-time driving applications while reducing processing time significantly.
Solution Approach 2:
The patent performs preliminary extraction of boundary lines and motion paths before any further processing or analysis. By pre-identifying these critical features directly from the image data without first performing full semantic segmentation, the system eliminates unnecessary computational steps and reduces overall processing time while maintaining the precision needed for safety-critical detections.
3Measurement precision
If high-dimensional image data is processed in full detail, then measurement precision is improved, but bandwidth consumption and memory requirements increase
Solution Approach 1:
The patent extracts only the essential geometric information (boundary lines and motion paths) from high-dimensional image data, storing and processing only these extracted features rather than the complete original image data. This extraction dramatically reduces memory requirements and bandwidth consumption while preserving the detection precision needed for identifying delimited regions and object motion paths.
Solution Approach 2:
The patent transforms the problem from processing high-dimensional pixel data to processing low-dimensional geometric representations. By representing delimited regions as boundary lines and objects as motion paths (essentially 1D or 2D geometric primitives rather than full 2D/3D image data), the system achieves the same detection objectives with significantly reduced data dimensions, memory requirements, and bandwidth consumption.
Data Source
AI summary
An apparatus for ascertaining, from at least one image, a delimited region and/or a motion path of at least one object includes at least one artificial neural network (ANN) made up of several successive layers. The first layer of the ANN receives as input the at least one image or a part thereof. The second layer supplies as output a boundary line of the delimited region, a linear course of the motion path, or a portion of the boundary line or motion path. The dimensionality of the second layer is lower than the dimensionality of the first layer.