Map Update Device Handling New Roads Without Common Feature Points
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Solution Overview
Problem
Existing map update devices struggle to update reference maps when the input map includes feature points not present in the reference map, such as newly opened roads, due to the lack of common feature points for position correction.
Innovation Solution
The map update device includes an input map generation section that creates an input map with landmarks and lane markings from probe data, a position correction section that aligns the input map with the reference map using feature points, and a difference detection section that updates the reference map by reflecting detected differences. Additionally, if position correction fails, the device estimates feature point positions, accumulates data, determines convergence, generates difference data, and reflects it in the reference map to update it.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position correction is performed using feature points, then map alignment accuracy is improved, but the system cannot handle new feature points not present in the reference map
Solution Approach 1:
The system performs preliminary position correction using existing common feature points before detecting differences. This preliminary alignment establishes a baseline for comparison, allowing the system to subsequently identify new feature points that were not present in the reference map through difference detection between the input map and reference map.
Solution Approach 2:
The system introduces difference detection as an intermediary step between position correction and map updating. This intermediary mechanism identifies changes including new feature points by comparing the position-corrected input map with the reference map, enabling the system to handle new roads and features without requiring them to be present during the initial feature point matching stage.
2Reliability
If the system requires common feature points for position correction, then alignment reliability is improved, but updating new roads becomes impossible
Solution Approach 1:
The system segments the map updating process into two independent stages: first, position correction using common feature points for reliable alignment; second, difference detection to identify new feature points and changes. This segmentation allows each stage to optimize for its specific function while collectively achieving both reliability and productivity.
Solution Approach 2:
The system performs preliminary position correction using reliable common feature points before conducting difference detection. This preliminary action ensures accurate alignment while enabling subsequent detection of new feature points, thereby maintaining reliability in the alignment process while achieving productivity in detecting new roads and features.
3Manufacturing precision
If difference detection is performed after position correction, then update accuracy is improved, but the system fails when position correction fails
Solution Approach 1:
The system performs preliminary position correction to achieve accurate alignment before conducting difference detection for high update accuracy. This sequential approach ensures that when position correction succeeds, the subsequent difference detection operates on properly aligned maps, maximizing update accuracy while maintaining system robustness through the fallback mechanism.
Solution Approach 2:
The system prepares a fallback mechanism beforehand by accumulating feature point positions from multiple input maps when position correction fails. This prior cushioning ensures that even if initial position correction fails, the system can still perform difference detection using accumulated data, thereby maintaining system robustness while preserving update accuracy when conditions permit.
4Measurement precision
If multiple input maps are accumulated for convergence determination, then position estimation accuracy is improved, but processing time increases
Solution Approach 1:
The system implements feedback through convergence determination, where feature point positions from accumulated input maps are continuously refined and compared. This feedback mechanism improves position estimation accuracy by identifying stable, convergent positions across multiple maps while providing a criterion to stop accumulation when convergence is achieved, thereby managing processing time efficiently.
Solution Approach 2:
The system performs preliminary position correction using common feature points before accumulating multiple input maps for convergence determination. This preliminary action reduces the number of maps needed for convergence by pre-establishing accurate alignments, thereby improving feature point position accuracy while minimizing the processing time required for accumulating and analyzing multiple maps.
Data Source
AI summary
A server includes a feature point position estimation section that estimates positions of feature points of a plurality of respective input maps if a position correction section has failed in position correction for an input map, an accumulated data generation section that accumulates the positions of the feature points of the plurality of respective input maps to generate accumulated data, a convergence determination section that determines whether the positions of the feature points have converged based on the accumulated data, a difference data generation section that generates, if it is determined that the positions of the feature points have converged, difference data from the positions of the feature points that have converged, and a difference data reflection section that reflects the difference data in the reference map to update the reference map.