Map Data Correction for Vehicle Lane Positioning
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Solution Overview
Problem
Existing map data correction methods fail to accurately position a subject vehicle with respect to lane information due to errors in the actual position and direction of lanes compared to the map data, leading to inaccurate vehicle positioning.
Innovation Solution
A map data correcting device and method that includes a position detection system, sensor unit, and controller to detect and correct errors between actual lane boundary lines and those in the map data by calculating uniform and distorted errors, allowing for precise alignment and offset of map data to reduce deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If map data is corrected using a single correction step (shifting and rotating), then the correction process is simple, but the position accuracy of the subject vehicle with respect to the lane cannot be improved
Solution Approach 1:
The correction process is divided into two distinct segments: a first correction process that shifts and rotates map data based on vehicle position and orientation, and a second correction process that performs additional shifting based on detected lane boundary deviations. This segmentation allows each correction step to address specific types of errors independently, achieving higher overall positioning accuracy without creating a monolithic complex system.
Solution Approach 2:
The first correction process is performed as a preliminary action before the second correction process. By initially aligning the map data using vehicle position and orientation information, the system establishes a baseline correction that simplifies subsequent lane boundary comparison and enables more focused secondary corrections based on actual lane detection deviations.
2Productivity
If map data is shifted and rotated in a single correction step, then the correction operation is efficient, but the degree of deviation between actual lane and mapped lane cannot be sufficiently reduced
Solution Approach 1:
The lane alignment process is segmented into two corrective operations: the first correction process handles gross misalignments through shifting and rotating based on vehicle pose, while the second correction process addresses residual deviations by detecting actual lane boundaries and applying additional shifts. This segmentation maintains operational efficiency by automating both steps while achieving high precision through the cumulative effect of targeted corrections.
Solution Approach 2:
The system implements feedback by detecting actual lane boundary lines using sensors, comparing them with corrected map data, and using the detected deviations to drive the second correction process. This closed-loop feedback mechanism ensures that corrections are continuously optimized based on real-world observations, achieving high lane alignment precision while maintaining efficiency through automated iterative correction.
3Loss of time
If only a single correction step is used, then the processing time is reduced, but the accuracy of positioning the subject vehicle with respect to the lane deteriorates
Solution Approach 1:
The correction timeline is segmented into two phases: the first correction process quickly establishes baseline alignment using pre-existing vehicle position and orientation data, while the second correction process performs more detailed adjustments based on real-time lane boundary detection. This temporal segmentation allows the system to balance processing time investment against positioning precision requirements at each stage.
Solution Approach 2:
The first correction process serves as a preliminary action that rapidly reduces large deviations using vehicle pose information before more time-intensive lane boundary detection and secondary correction are performed. This preliminary correction minimizes the workload for subsequent processing steps, optimizing the overall time-precision tradeoff.
Data Source
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AI summary
A map data correcting method for correcting map data used in a vehicle using a controller (110) is provided. This method includes executing a first correction process of uniformly offsetting the map data as a whole to reduce a first error that is a general position error of the map data and executing a second correction process of reducing a second error that is a position error still remaining in the map data even after uniformly offsetting the map data as a whole.