Map Element Warping for Trajectory Alignment Updates
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Solution Overview
Problem
Existing map data updating techniques require significant resources and manual intervention, leading to inefficient and potentially unsafe navigation for autonomous vehicles due to incorrect registration of map elements with updated data.
Innovation Solution
The techniques involve determining an alignment between a first and second trajectory using algorithms like SLAM or CLAMS, generating an updated trajectory, and warping map elements based on transformations to ensure accurate registration with updated map data, automating the process and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If map elements are updated manually to reflect changes in map data, then accuracy of map element registration is improved, but processing time and resource consumption increase significantly
Solution Approach 1:
The system automatically updates map elements by detecting trajectory changes and applying transformations without manual intervention. The autonomous vehicle's own trajectory data serves to update the map elements, eliminating the need for external manual updating while maintaining accuracy.
Solution Approach 2:
The system pre-computes transformation parameters based on trajectory alignments between different map data versions. By preparing these transformations in advance and applying them automatically when trajectory changes are detected, the system avoids time-consuming manual updates while ensuring accurate registration.
2Reliability
If map elements are updated frequently to maintain accuracy with updated map data, then navigation safety is improved, but computational resources and processing complexity increase
Solution Approach 1:
The system applies transformations locally to only those map elements that are affected by trajectory changes, rather than updating the entire map dataset. This selective updating approach maintains navigation safety for relevant elements while reducing overall processing complexity and resource consumption.
Solution Approach 2:
The update process is segmented into distinct steps: trajectory alignment, transformation parameter computation, and selective application to affected map elements. This segmentation allows the system to maintain high reliability through systematic processing while reducing complexity by breaking down the overall task into manageable, automated stages.
3Productivity
If automated trajectory alignment algorithms are used to update map elements, then processing efficiency is improved, but precision of map element registration may deteriorate
Solution Approach 1:
The system uses feedback from trajectory alignment results to refine transformation parameters. By comparing the aligned trajectories and using this information to adjust the warping of map elements, the automated process achieves both high processing efficiency and accurate registration, eliminating the trade-off between speed and precision.
Data Source
AI summary
Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.