Lane Line Regression Detection With Spatially Precise Reconstruction
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Solution Overview
Problem
Conventional lane detection systems for autonomous vehicles suffer from loss of spatial information due to down-sampling in deep neural networks, leading to reduced precision and accuracy, and require significant processing power and resources, making real-time deployment challenging.
Innovation Solution
A regression-based line detection system that preserves spatial information by training machine learning models to predict distances and angles at a lower output resolution, allowing for precise recreation of lines at the input resolution, and uses bit encoding and embedding algorithms to reduce computational burden.
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 spatial information is lost due to down-sampling, reducing detection precision
Solution Approach 1:
The patent transforms the detection task from direct pixel classification to predicting distance and angle parameters. Instead of classifying each pixel as lane or non-lane, the system predicts the distance to the nearest lane pixel and the angle of the lane at that point. This dimensional transformation allows the model to work at lower resolution while preserving the ability to reconstruct precise lane geometries through integration of distance and angle information.
2Reliability
If conventional DNN approaches are used, then lane detection can be performed, but significant processing power and compute resources are required, making real-time deployment difficult
Solution Approach 1:
The patent extracts only the essential geometric parameters needed for lane detection—distance to lane and angle of lane—rather than processing full segmentation masks. By removing unnecessary intermediate processing steps and focusing on predicting only the critical parameters, the system significantly reduces computational burden while maintaining detection accuracy, enabling real-time deployment on autonomous vehicles.
3Device complexity
If down-sampling is performed in DNN processing, then computational complexity is reduced, but critical spatial information is lost, reducing accuracy
Solution Approach 1:
The patent changes the output parameters from binary classification labels to continuous distance and angle values. This parameter transformation allows the model to capture precise spatial relationships even at lower resolutions, as distance and angle are invariant to the resolution at which they are measured. The continuous nature of these parameters preserves spatial information that would be lost in discrete pixel classification.
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.