Spline-Based Lane Boundary Tracking for Real-Time Vehicle Vision
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and predicting lane boundary positions in real-time using on-board imaging sensors and computer vision techniques, especially at higher speeds, which can lead to drifting into adjacent lanes or collisions.
Innovation Solution
The method involves receiving pixels from an imaging system, partitioning them into groups associated with control points, and generating a spline that traverses these control points to describe lane boundaries. This approach reduces computational resources by focusing on control points rather than all pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data collection and analysis is performed using on-board imaging sensors and computer vision techniques, then lane boundary position determination is achieved, but computational resources are excessively consumed and processing time is insufficient at higher speeds
Solution Approach 1:
The patent segments the pixel data by dividing the image into multiple regions of interest (such as left lane, right lane, center line regions) and processing each region separately. This segmentation reduces the overall computational load while maintaining accurate lane boundary detection in each specific region, resolving the contradiction between processing speed and measurement precision.
Solution Approach 2:
The patent extracts only the essential features needed for lane boundary detection (such as edge pixels, gradient information, or specific pixel patterns) rather than processing all pixel data. By taking out only the critical information required for lane determination, the system achieves accurate measurement while significantly reducing computational resource consumption and increasing processing speed.
2Measurement precision
If all pixels are processed to identify lane boundary positions, then detection accuracy is improved, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features needed for lane boundary detection (such as edge pixels, gradient information, or specific pixel patterns) rather than processing all pixel data. By taking out only the critical information required for lane determination, the system achieves accurate measurement while significantly reducing computational resource consumption.
Solution Approach 2:
The patent segments the pixel data by dividing the image into multiple regions of interest and processing each region separately with simplified algorithms. This segmentation reduces the overall computational load while maintaining accurate lane boundary detection in each specific region, resolving the contradiction between processing accuracy and device complexity.
3Loss of information
If conventional computer vision techniques are used to detect lane boundaries, then detection capability is achieved, but the system cannot predict lane positions beyond the imaging system's range
Solution Approach 1:
The patent performs preliminary analysis of detected lane boundary patterns, curvatures, and trends to predict future lane positions beyond the current imaging range. By taking preliminary actions to extrapolate lane information based on detected patterns and vehicle motion characteristics, the system compensates for the limited detection range while maintaining detection efficiency through selective prediction rather than exhaustive detection.
Data Source
AI summary
A system for detecting boundaries of lanes on a road is presented. The system includes an imaging system configured to produce a set of pixels associated with lane markings on a road. The system also includes one or more processors configured to detect boundaries of lanes on the road, including: receive, from the imaging system, the set of pixels associated with lane markings; partition the set of pixels into a plurality of groups, each of the plurality of groups associated with one or more control points; and generate a first spline that traverses the control points of the plurality of groups, the first spline describing a boundary of a lane on the road.