Spatial Template Deformation for Accurate Vehicle Landmark Positioning
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Solution Overview
Problem
Existing systems face challenges in accurately identifying landmarks due to varying sensor data from different vehicles, leading to positioning discrepancies and reduced safety in automated driving systems.
Innovation Solution
A system that generates and deforms a spatial template using trace data from multiple vehicles, employing a learning model and a spring model to align landmark positions, filling in missing data and maintaining spatial relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data is collected from multiple vehicles at different positions, then the coverage and awareness of the surrounding environment is improved, but the positioning accuracy of landmarks deteriorates due to offsets and discrepancies
Solution Approach 1:
A spatial template serves as an intermediary data structure that mediates between the disparate sensor data from multiple vehicles. The template acts as a common reference frame that can accommodate measurements from different positions while maintaining consistency, thereby resolving the contradiction between multi-vehicle coverage and positioning accuracy.
Solution Approach 2:
The spatial template is dynamically deformed using a spring model that adapts to the specific geometric relationships between vehicles and landmarks. This dynamic deformation allows the system to maintain positioning accuracy despite the varying positions and perspectives of multiple vehicles, resolving the offset problem while preserving environmental awareness.
2Measurement precision
If a rigid spatial template is used to maintain consistent landmark positions, then positioning accuracy is improved, but the system cannot accommodate variations and missing data from different vehicle perspectives
Solution Approach 1:
The spatial template transitions from a rigid structure to a dynamic one that can deform according to the spring model. This allows the template to adapt to variations in sensor data from different vehicles while maintaining overall structural integrity and positioning accuracy, thereby accommodating missing data and perspective variations.
Solution Approach 2:
The spring model changes the parameters of the spatial template by introducing elastic deformation capabilities. This allows the template to flex and adjust its geometry based on the input data from different vehicles, maintaining accuracy while adapting to various perspectives and filling in missing information.
3Device complexity
If landmark locations are directly aligned without deformation, then the processing complexity is reduced, but the spatial relationships between landmarks deteriorate due to perspective differences
Solution Approach 1:
The spring model introduces dynamic deformation that automatically adjusts landmark positions to maintain correct spatial relationships. This dynamic approach handles perspective differences without requiring complex manual processing, thereby preserving spatial integrity while managing processing complexity through automated elastic deformation.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to identifying landmarks by generating and deforming a spatial template using trace data from multiple vehicles. In one embodiment, a method includes forming a trace dataset for multiple vehicles from locations of landmarks and vehicles identified with sensor data by a learning model, the sensor data associated with the multiple vehicles. The method also includes generating a spatial template by a vehicle using the trace dataset having missing data. The method also includes aligning the landmarks for positioning by deforming the spatial template using a spring model that fills the missing data.