Map Curve Alignment for Autonomous Feature Change Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately determining changes in their environment, particularly when relying on pre-stored map information that may not be up-to-date or accurate.
Innovation Solution
The method involves receiving data on detected objects in a vehicle's environment, identifying corresponding features from pre-stored map information, and adjusting the position of curve segments based on location coordinates and tolerance constraints to determine the likelihood of feature changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-stored map information is used for environment perception, then navigation efficiency is improved, but accuracy of environmental change detection deteriorates
Solution Approach 1:
The curve representing the map feature is divided into multiple line segments. Each segment can be independently adjusted and compared with sensor data, enabling precise detection of environmental changes while maintaining overall navigation efficiency through structured processing.
Solution Approach 2:
The system dynamically adjusts the position of line segments by shifting and rotating them based on sensor data and tolerance constraints. This dynamic adjustment allows the map information to adapt to environmental changes, improving detection accuracy while maintaining navigation efficiency.
2Reliability
If tolerance constraints are applied to adjust curve segments, then reliability of feature matching is improved, but complexity of processing increases
Solution Approach 1:
Different tolerance constraints are applied to different line segments based on their specific characteristics and importance. This localized approach improves matching reliability for critical segments while avoiding unnecessary processing complexity for less important segments.
Solution Approach 2:
The system changes geometric parameters (position, orientation) of line segments within defined tolerance constraints. By adjusting these parameters systematically, the system achieves reliable feature matching without requiring complex processing algorithms.
3Measurement precision
If line segments are shifted and rotated to align with sensor data, then accuracy of position determination is improved, but computational time increases
Solution Approach 1:
The system performs preliminary adjustments to line segment positions and orientations before final comparison with sensor data. By pre-positioning segments within tolerance constraints, the system reduces the computational time required for final alignment while maintaining high accuracy in position determination.
Data Source
AI summary
Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding object. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.