Lane Marker Recognition With Multi-Headed CNN Regression
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Solution Overview
Problem
Existing lane marker detection systems in vehicles are slow, require significant manual configuration, lack robustness across various driving scenarios, and consume excessive computational resources, making them unsuitable for modern vehicles.
Innovation Solution
A multi-headed machine learning architecture that includes separate components for lane marker localization, horizontal position estimation, and end position detection, which aggregates data to generate accurate lane marker instances efficiently, reducing computational complexity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image processing techniques are used for lane marker recognition, then the system is simple to implement, but the system cannot recognize lane markers under varying illumination conditions
Solution Approach 1:
The patent transforms lane marker detection from conventional 2D image space to 3D point cloud space by changing the parameter dimension. The laser radar captures spatial coordinates (x, y, z) instead of 2D pixel values, and the system processes depth information combined with reflectivity data to identify lane markers regardless of illumination conditions.
Solution Approach 2:
The patent replaces conventional optical image processing with laser radar-based 3D point cloud processing. Instead of using cameras that rely on visible light and complex illumination compensation algorithms, the system uses laser ranging to directly measure spatial positions, substituting optical-mechanical detection with laser-based spatial measurement.
2Measurement precision
If the system uses detailed 3D point cloud data for accurate lane marker recognition, then recognition accuracy improves, but processing time increases
Solution Approach 1:
The patent segments the 3D point cloud data processing into distinct stages: raw point cloud acquisition, preliminary filtering to remove irrelevant points, lane marker region identification, and parameter extraction. This segmentation allows the system to process only relevant portions of the data at each stage, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent extracts only the essential features needed for lane marker recognition from the complete 3D point cloud data. Instead of processing all point cloud information, the system identifies and extracts key characteristics such as point density patterns, spatial distribution features, and reflectivity profiles specific to lane markers, discarding redundant data.
3Measurement precision
If the system processes raw 3D point cloud data without preprocessing, then data integrity is maintained, but noise and irrelevant points affect recognition accuracy
Solution Approach 1:
The patent introduces an intermediary preprocessing module between the laser radar and the lane marker recognition algorithm. This intermediary layer performs functions such as outlier removal, density-based clustering, and spatial filtering to eliminate noise and irrelevant points while preserving genuine lane marker features, acting as a mediator that cleans data without losing essential information.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate, robust, and efficient lane marker detection, handling complex geometries like splits and merges, with reduced latency and power consumption, enhancing safety and reliability in autonomous driving systems.
Implementation Method 1
a laser radar device to capture images of the road surface in front of the vehicle
Data Source
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AI summary
Certain aspects of the present disclosure provide techniques for lane marker detection. A set of feature tensors is generated by processing an input image using a convolutional neural network. A set of localizations is generated by processing the set of feature tensors using a localization network, a set of horizontal positions is generated by processing the set of feature tensors using row-wise regression, and a set of end positions is generated by processing the set of feature tensors using y-end regression. A set of lane marker positions is determined based on the set of localizations, the set of horizontal positions, and the set of end positions.