Lane Recognition Deviation Control Using Curvature Second Derivative
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Solution Overview
Problem
Existing lane recognition systems using vehicle-mounted cameras face reduced accuracy in estimating the shape of complex travel lanes, such as those with alternating curves, due to noise picked up by high responsivity filters, leading to unstable estimation and decreased control accuracy.
Innovation Solution
An apparatus that includes an image acquirer, edge extractor, travel lane parameter estimator, shape change point extractor, and deviation determiner, which extracts shape change points and determines if the extracted boundary line deviates from the estimated boundary line beyond a predetermined range, triggering control measures to prevent undesirable situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a filter with high responsivity is used to estimate travel lane parameters, then the estimation can follow changes in shape of crooked lanes, but the filter picks up noise leading to unstable estimation
Solution Approach 1:
The system performs preliminary extraction of shape change points based on curvature second derivative values before the parameter estimation process. By identifying these critical points in advance and using them to determine whether to apply deviation control, the system prepares the necessary information beforehand to adjust filter responsivity appropriately, preventing noise pickup while maintaining ability to follow lane shape changes.
2Reliability
If the responsivity of the filter is set low to prevent unstable estimation, then noise is reduced, but estimation fails to follow changes in shape of crooked lanes
Solution Approach 1:
The system dynamically adjusts the application of deviation control based on real-time detection of shape change points. When shape change points are detected (indicating crooked lane sections), the system activates deviation control to improve tracking accuracy. When no shape change points are present (straight lane sections), the system maintains low filter responsivity for stable estimation. This dynamic adjustment allows the system to optimize both stability and accuracy according to actual lane conditions.
3Measurement precision
If deviation control is performed based on shape change points, then accuracy of estimating complex lane shapes is improved, but device complexity increases
Solution Approach 1:
The system segments the lane detection problem by identifying shape change points that divide the lane into sections with different characteristics (straight vs. crooked). By processing the lane in these segments rather than uniformly across the entire lane, the system can apply deviation control only where necessary (at shape change points), improving accuracy for complex lane shapes while avoiding unnecessary processing in simple sections, thus managing system complexity.
Data Source
AI summary
In an apparatus for recognizing a travel lane of a vehicle, a deviation determiner is configured to, if two or more shape change points are extracted by a shape change point extractor and the second derivative value of curvature of an extracted boundary line at at least one of the two or more shape change points is inverted in sign, determine whether or not the extracted boundary line and an estimated boundary line estimated from travel lane parameters estimated by a travel lane parameter estimator are deviating from each other beyond a predetermined allowable range. A driving aid is configured to, if it is determined that the extracted boundary line and the estimated boundary line are deviating from each other beyond the predetermined allowable range, perform control upon deviation to prevent occurrence of undesirable situations that may be caused by deviation between the extracted boundary line and the estimated boundary line.