Lane Recognition Device Using Segmented Cubic Equations for Curved Lines
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Solution Overview
Problem
Conventional lane recognition technologies fail to accurately recognize lines on the road when they are curved at a certain angle, leading to increased errors and compromised driving safety.
Innovation Solution
A lane recognition device and method that captures images using a camera and converts them into a road coordinate system, detecting curved points by generating multiple cubic equations based on error graphs and adjusting thresholds for curve fitting, especially when the curvature rate exceeds a certain threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single cubic equation is used to represent the lane line, then the device complexity is low, but the measurement precision deteriorates when the line forms sharp angles
Solution Approach 1:
The lane line detection model is segmented into multiple cubic equations, each representing a different section of the lane line. The controller divides the detected line into multiple segments and applies curve fitting to each segment separately, allowing accurate representation of sharp angles and curved portions while maintaining computational feasibility.
Solution Approach 2:
The system dynamically determines the number and positioning of cubic equations based on the detected line characteristics. When sharp angles or curved portions are detected, the system automatically increases the number of cubic equations and adjusts their distribution to match the line's geometry, making the model adaptive rather than static.
2Measurement precision
If multiple cubic equations are used to represent the lane line, then the measurement precision improves for curved lines, but the device complexity increases
Solution Approach 1:
The system dynamically adjusts the number of cubic equations based on the detected line characteristics. When sharp angles or curved portions are detected, the system automatically increases the number of cubic equations and adjusts their distribution to match the line's geometry, making the model adaptive rather than static.
Solution Approach 2:
The system changes the parameter of the number of cubic equations based on the curvature and angle characteristics of the detected line. By adjusting this parameter dynamically, the system achieves high precision for complex line geometries while avoiding unnecessary computational complexity for simple straight lines.
3Measurement precision
If curve fitting is applied to the entire line, then the processing is simple, but the measurement precision deteriorates at curved portions
Solution Approach 1:
The curve fitting process is segmented into multiple sections, with each cubic equation fitted to a specific segment of the lane line. This segmentation allows the system to capture local geometric features such as sharp angles and curved portions accurately, while the overall processing remains systematic and manageable.
Data Source
AI summary
A lane recognition device includes: a camera configured to capture an image in front of a vehicle; and a controller configured to detect a lane from the image in front of the vehicle; generate a plurality of lane equations based on a curved point of the lane; and recognize the lane based on the plurality of lane equations.