Autonomous Vehicle Line Classification Using Eigenvector Point Analysis
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Solution Overview
Problem
Existing vehicle control systems face challenges in accurately determining lines in the surrounding environment for autonomous driving, particularly due to issues like crosstalk and false object detection, which can lead to misinterpretation of sensor data.
Innovation Solution
A vehicle control apparatus and method that utilizes a processor to analyze sensor data using eigen vectors and eigen values to differentiate between points representing lines and non-line objects, applying algorithms like singular value decomposition and eigen decomposition to identify and flag points as lines, thereby preventing incorrect object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor data processing methods are used to determine lines in the surrounding environment, then the processing speed is maintained, but the accuracy of line detection deteriorates due to crosstalk and false object detection
Solution Approach 1:
The patent segments the sensor data processing by dividing points into different types (first type points and second type points) based on their spatial distribution characteristics. This segmentation allows different processing approaches to be applied to different point types, improving line detection accuracy while managing computational complexity through targeted analysis of specific point subsets.
Solution Approach 2:
The patent changes the processing parameters by applying eigenvalue and eigenvector analysis to transform the original sensor data into a new coordinate system. This parameter transformation enables the system to identify line characteristics more accurately by analyzing the directional properties of point distributions, thereby resolving crosstalk issues without requiring overly complex processing.
2Measurement precision
If eigenvalue and eigenvector analysis is applied to all points to improve line detection accuracy, then the detection precision improves, but the computational time increases
Solution Approach 1:
The patent applies eigenvalue and eigenvector analysis selectively rather than to all points. By identifying second type points through initial spatial distribution analysis and then applying the computationally intensive eigenanalysis only to these specific points, the system achieves high classification accuracy while minimizing overall processing time through partial application of the advanced algorithm.
Solution Approach 2:
The patent performs preliminary classification of points into different types based on spatial distribution before applying the more complex eigenvalue and eigenvector analysis. This preliminary action filters the data set, ensuring that the computationally intensive processing is applied only where necessary, thereby reducing total processing time while maintaining high accuracy for critical point classifications.
3Reliability
If the number of processing steps is increased to reduce false detections, then the reliability of object classification improves, but the device complexity increases
Solution Approach 1:
The patent introduces eigenvalues and eigenvectors as intermediary parameters that mediate between the raw sensor data and the final object classification. These intermediaries provide a mathematical framework that systematically evaluates point distributions, enabling reliable differentiation between lines and non-line objects through a structured multi-step process that manages complexity through formalized intermediate representations.
Data Source
AI summary
An apparatus for controlling autonomous driving of a vehicle is introduced. The apparatus comprises a sensor to capture multiple points representing the vehicle's surrounding environment and a processor to analyze these points. The processor maps the points onto a grid derived from a vehicle-centered coordinate system. Using a first algorithm, the processor identifies the locations of the points on the grid. A second algorithm calculates eigenvalues or eigenvectors based on points designated as a specific type. The processor checks if a predefined condition is met by evaluating factors such as the number of points in a grid, the direction of eigenvectors, and point locations. If the condition is satisfied, the processor determines that the points correspond to another designated type, based on the eigenvalues or eigenvectors. The apparatus then generates a signal indicating the classification of the points and controls the vehicle's autonomous driving based on such information.