Augmented 3D Map Overlay for Autonomous Vehicle Mis-Localization
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Solution Overview
Problem
Autonomous vehicles face navigation issues due to inconsistencies between high-resolution primary maps and current road conditions, leading to mis-localization, especially when environmental changes like construction occur, as they rely on outdated maps until a high-resolution update is available.
Innovation Solution
The system dynamically updates a portion of the map database using low-resolution data from the autonomous vehicle's sensors to create an augmented map, overlaying new road features on the primary map, allowing continuous navigation without requiring immediate updates to the high-resolution primary map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses the high-resolution primary map for navigation, then the navigation accuracy is improved, but the map becomes outdated when road conditions change, leading to mis-localization
Solution Approach 1:
The system performs preliminary actions by continuously capturing sensor data and detecting inconsistencies before they cause significant navigation errors. The autonomous vehicle proactively identifies when road conditions differ from the primary map and triggers map updates in advance, preventing mis-localization rather than reacting to it after it occurs.
Solution Approach 2:
The system implements a feedback mechanism where sensor data from the autonomous vehicle is continuously compared against the primary map. When inconsistencies are detected (such as construction zones or road changes), this feedback triggers an automated process to update the map database, ensuring the navigation system adapts to current road conditions while maintaining the benefits of high-resolution mapping.
2Measurement precision
If the autonomous vehicle waits for high-resolution map updates, then the navigation accuracy is maintained, but the vehicle must restrict operation in areas with outdated maps
Solution Approach 1:
The system applies partial action by updating only the specific portions of the map database that contain inconsistencies, rather than requiring complete high-resolution remapping of entire regions. This allows the autonomous vehicle to continue operating in updated areas while the rest of the map gradually gets updated, maintaining navigation accuracy where needed without restricting overall operational availability.
Solution Approach 2:
The map database is segmented into manageable portions or tiles that can be independently updated. When inconsistencies are detected in specific geographic areas, only those segments are updated using sensor data from the autonomous vehicle, allowing other segments to remain operational with their existing high-resolution data, thus maintaining productivity while improving accuracy in critical areas.
3Productivity
If the system uses low-resolution sensor data to update the map, then the map can be updated frequently without restriction, but the update precision is reduced
Solution Approach 1:
The system merges low-resolution sensor data from the autonomous vehicle with the existing high-resolution primary map data during the update process. By combining these data sources, the system maintains the high precision of the original map where available while incorporating recent changes detected by sensors, achieving both frequent updates and high precision in the augmented map database.
Solution Approach 2:
The system applies local quality by using high-resolution data from the primary map in areas where it remains accurate and unchanging, while using low-resolution sensor data only in specific locations where inconsistencies have been detected. This selective approach ensures high update precision in critical areas while allowing frequent updates elsewhere, optimizing both precision and productivity locally rather than uniformly across the entire map.
Data Source
AI summary
The present technology provides systems, methods, and devices that can dynamically augment aspects of a map as an autonomous vehicle navigates a route, and therefore avoids the need for dispatching a special purpose mapping vehicle to keep navigating a route. As the autonomous vehicle navigates a route, the autonomous vehicle can determine that current data captured by at least one sensor of the autonomous vehicle describing a location is inconsistent with the primary map of the location. The autonomous vehicle can determine that a portion of the current data describes a second feature that is distinct from a first feature described by a primary map. The second feature can be added to an augmented map that is based on the primary map, and the position of the autonomous vehicle can be located with respect to the first feature rather than the second feature on the augmented map.