Road Boundary Point Segmentation for Multi-Road Recognition
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Solution Overview
Problem
Existing road boundary recognition methods struggle with accuracy, particularly in complex environments like intersections or ramp junctions where multiple roads are present, leading to inaccurate curve fitting and failure to distinguish between different road boundaries.
Innovation Solution
A method involving clustering road boundary points, identifying jump groups based on preset distances, and segmenting these groups to form distinct sets corresponding to individual road boundaries, followed by curve fitting specific to each set to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If road boundary points from multiple roads are clustered into a single set, then the clustering process is simplified, but the curve fitting accuracy deteriorates because the fitted curve cannot accurately represent individual road boundaries
Solution Approach 1:
The patent segments the clustered road boundary points into multiple subsets, where each subset corresponds to a single road boundary. This is achieved by detecting jump groups (points with large distance gaps) and splitting the set at these jump points. The segmentation enables accurate curve fitting for each individual road boundary while maintaining the overall clustering structure.
Solution Approach 2:
The patent applies different processing quality to different parts of the road boundary point set. By identifying jump groups and creating separate subsets for different road boundaries, each subset receives localized curve fitting processing appropriate to its specific road geometry, improving overall recognition accuracy.
2Device complexity
If a single curve expression is used to fit road boundary points from multiple roads, then the mathematical modeling is simplified, but the fitting accuracy deteriorates due to large deviations from actual road boundaries
Solution Approach 1:
The patent divides the road boundary point set into multiple subsets based on jump group detection, where each subset is then fitted with an appropriate curve expression. This segmentation approach allows each curve to accurately represent its corresponding road boundary segment without the complexity of a single universal curve model.
Solution Approach 2:
Instead of attempting to fit all road boundary points with a single curve, the patent applies curve fitting partially to each subset separately. This partial action approach achieves better overall accuracy by allowing each curve to focus on its specific road boundary segment.
Data Source
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AI summary
This application relates to the field of intelligent driving technologies, and provides a road boundary recognition method and apparatus. The method includes: clustering a plurality of road boundary points to obtain a plurality of sets, where a first set in the plurality of sets includes at least one jump group, and each jump group includes a first road boundary point and a second road boundary point; sorting, in a first direction, a plurality of road boundary points in the first set, where a distance between the first road boundary point and the second road boundary point is greater than or equal to a preset distance; and segmenting, based on the at least one jump group, the first set into a second set and a third set that correspond to different road boundaries. The sets are segmented, and the two sets obtained after the segmentation correspond to different road boundaries, so that road boundary recognition can be more accurate. The method improves an advanced driver assistance systems ADAS capability of a terminal in autonomous driving or assisted driving, and may be applied to the Internet of Vehicles, for example, vehicle-to-everything V2X, long-term evolution-vehicle LTE-V, and vehicle-to-vehicle V2V.