Lane Line Matching Using Correlation Weights for Vehicle Localization
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Solution Overview
Problem
Accurate vehicle localization in autonomous driving systems is hindered by sensor errors and false lane line detections due to road conditions and interference, leading to mismatched lane line data between sensed and map data.
Innovation Solution
A method and apparatus that construct a correlation weight matrix based on differences between sensed lane lines and map lane lines, including horizontal intercepts, relative yaw angles, and control point offsets, to optimize the matching process and reduce interference from misdetected lane lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane line matching methods are used, then the matching process is simple, but the accuracy of vehicle localization deteriorates due to sensor errors and false detections
Solution Approach 1:
The patent segments the lane line matching process into multiple independent stages: feature extraction from sensed lane lines, construction of correlation weight matrix based on geometric parameters (horizontal intercept, relative yaw angle, control point offset), and iterative optimization matching. This segmentation allows each stage to focus on specific aspects, improving overall accuracy while managing complexity through modular processing.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating geometric parameters (horizontal intercept, relative yaw angle, control point offset) and constructing the correlation weight matrix before the actual matching process. This preliminary preparation filters out obviously mismatched lane lines early, reducing the search space and improving final localization accuracy without proportionally increasing overall complexity.
2Reliability
If all sensed lane line data is used for matching, then more data is available for localization, but false detections and interference increase matching errors
Solution Approach 1:
The patent changes parameters by introducing a correlation weight matrix that evaluates multiple geometric parameters (horizontal intercept difference, relative yaw angle difference, control point offset) simultaneously. Lane lines are filtered and weighted based on these parameter comparisons, allowing the system to distinguish reliable detections from false ones. This parameter-based filtering significantly improves data reliability while reducing the impact of false detections.
3Measurement precision
If complex matching algorithms are used to improve accuracy, then localization precision increases, but calculation complexity and processing time increase
Solution Approach 1:
The patent performs preliminary filtering and weight calculation before the main matching algorithm. By pre-calculating the correlation weight matrix based on geometric parameter differences, the system reduces the number of candidate lane lines that need to be processed in the optimization stage. This preliminary action maintains high matching accuracy while significantly reducing the computational burden of the subsequent optimization process.
Data Source
AI summary
The present disclosure provides a method of matching lane line data, which relates to a field of automatic driving, and in particular to a field of locating an autonomous vehicle. The method of matching lane line data includes: obtaining a first data indicating sensed lane lines and a second data indicating map lane lines respectively; constructing a correlation weight matrix for the first data and the second data, based on differences between the sensed lane lines and the map lane lines; and matching the first data with the second data according to the correlation weight matrix, so as to obtain a third data having an optimal match with the first data from the second data. The present disclosure further discloses an apparatus of matching lane line data, a device, a storage medium and a computer program product.