Regression-Based Line Detection for Low-Resolution Lane Precision
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Solution Overview
Problem
Conventional approaches to lane or line detection in autonomous vehicles suffer from loss of resolution due to down-sampling in deep neural networks, leading to reduced precision and accuracy, and require significant processing power and resources.
Innovation Solution
The system employs a regression-based line detection method that preserves rich spatial information by training a machine learning model to predict distances and angles for each pixel at an input resolution, allowing for precise recreation of lines even at lower output resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks are used for lane detection, then the system can process complex road markings and variations, but resolution is lost due to down-sampling operations
Solution Approach 1:
The patent transforms the output from a spatial grid of class predictions into a set of parametric line representations (slope, intercept, confidence scores). This dimensional transformation allows the network to work at lower resolutions while recovering precise line positions through mathematical parameterization rather than pixel-by-pixel classification.
Solution Approach 2:
Instead of predicting lane markings as pixel-level classifications, the system changes the output parameters to represent lines analytically (using slope-intercept form and confidence metrics). This parameter-based approach preserves precision by defining lines continuously rather than discretely at pixel boundaries.
2Adaptability or versatility
If conventional DNN approaches are used with separate output channels for each class, then comprehensive lane marking detection is achieved, but run-time increases and significant processing power is required
Solution Approach 1:
The patent extracts only the essential parameters needed for line detection (slope, intercept, confidence) from the full pixel-level classification output. By taking out only the critical information required for lane marking representation, the system reduces computational burden while maintaining detection comprehensiveness.
Solution Approach 2:
Instead of the conventional approach of classifying each pixel and then reconstructing lines, the system inverts the process by directly predicting line parameters from the image. This inversion eliminates the computationally expensive pixel-to-line reconstruction step while achieving the same detection goal.
3Device complexity
If down-sampling is performed during DNN processing, then computational complexity is reduced, but critical spatial information for inferring lane lines is lost
Solution Approach 1:
The system creates a parametric copy of the line information that is resolution-independent. By representing lines through mathematical parameters rather than pixel patterns, the essential spatial information is preserved in a compact form that does not require high-resolution input processing.
Data Source
AI summary
In various examples, systems and methods are disclosed that preserve rich spatial information from an input resolution of a machine learning model to regress on lines in an input image. The machine learning model may be trained to predict, in deployment, distances for each pixel of the input image at an input resolution to a line pixel determined to correspond to a line in the input image. The machine learning model may further be trained to predict angles and label classes of the line. An embedding algorithm may be used to train the machine learning model to predict clusters of line pixels that each correspond to a respective line in the input image. In deployment, the predictions of the machine learning model may be used as an aid for understanding the surrounding environment—e.g., for updating a world model—in a variety of autonomous machine applications.