Multi-Dimensional Lane Matching for Robust AV Localization
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Solution Overview
Problem
Current automated driving systems face challenges in accurately localizing vehicles and identifying road features, particularly in matching lane markings, due to limited information processing and lack of robustness in considering lane color, type, and curvature.
Innovation Solution
A method and system that processes perception input data and map data to extract lane information, calculates a transformation matrix, and generates a fitness score to improve vehicle localization by considering lane position, color, type, and curvature, using a weighted distance equation to match lane markings and update vehicle localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane matching methods are used, then the system is simpler to implement, but localization accuracy and robustness deteriorate due to limited information processing
Solution Approach 1:
The patent extends traditional 2D lane matching by incorporating multiple dimensions including lane color, lane type, and lane curvature alongside position coordinates. This multi-dimensional approach significantly improves localization accuracy by providing more discriminative features for matching perceived lane markings with map data, while the structured implementation keeps the complexity manageable.
Solution Approach 2:
The transformation matrix calculation framework serves multiple functions simultaneously: it performs coordinate transformation, evaluates matching quality through fitness scores, and integrates multiple lane features (position, color, type, curvature). This multi-functionality improves localization accuracy without proportionally increasing system complexity.
2Reliability
If multi-dimensional lane features are processed, then matching robustness improves, but computational complexity increases
Solution Approach 1:
The patent segments the lane matching process into distinct computational stages: extracting lane features (position, color, type, curvature), calculating transformation matrices, computing fitness scores, and updating localization. This segmentation improves matching robustness by systematically processing each feature dimension while keeping computational complexity manageable through organized workflow.
Solution Approach 2:
The patent transforms multiple lane features into a unified transformation matrix framework where each feature dimension (color, type, curvature) contributes to the overall matching evaluation. By changing parameters from separate processing streams to an integrated matrix formulation, the system achieves higher robustness without proportional increases in computational complexity.
3Measurement precision
If fitness score evaluation is implemented, then transformation matrix effectiveness is improved, but processing time increases
Solution Approach 1:
The patent implements fitness score evaluation that assesses transformation matrix effectiveness across multiple lane features. By performing partial evaluations (focusing on key discriminative features like color and curvature when available) rather than exhaustive analysis of all possible features, the system improves transformation accuracy while limiting processing time increases.
Data Source
AI summary
A method for updating a localization of an autonomous vehicle includes receiving perception input data from a sensor of the autonomous vehicle and receiving map data including road lane information in a vicinity of the autonomous vehicle. The method includes processing the perception input data to extract perceived road lane information including a perceived x position, a perceived y position, a perceived z position, a perceived lane type, a perceived lane color, and a perceived lane curvature and processing the map data to extract map road lane information including a map x position, a map y position, a map z position, a map lane type, a map lane color, and a map lane curvature. The method includes calculating a transformation matrix from the perceived road lane information and the map road lane information and updating the map data and a localization of the vehicle based on the transformation matrix.