Sparse Road Maps and Lane Measurements for AV Localization
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating roadways due to the sheer volume of data required for traditional mapping, which can limit navigation accuracy and efficiency, and there is a need for optimized map construction and storage solutions.
Innovation Solution
The use of cameras to construct and navigate with a crowdsourced sparse map, incorporating GPS and sensor data, and adaptive navigation systems that utilize lane measurements and lane markings for precise vehicle positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then navigation coverage and completeness are improved, but data storage requirements and system complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigation-critical information from traditional dense maps, specifically road geometry, lane markings, and key landmarks, while discarding redundant detailed map data. This creates a sparse map that retains navigation functionality with dramatically reduced data storage requirements.
Solution Approach 2:
The patent segments the map data into discrete, manageable components such as road segments, lane elements, and landmark markers rather than storing a continuous dense map. This segmentation allows the system to store only navigation-relevant information at appropriate levels of detail, reducing overall data volume while maintaining navigation accuracy.
2Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then map completeness is improved, but computation time and processing efficiency deteriorate
Solution Approach 1:
The patent extracts only the essential navigation-critical information from traditional dense maps, specifically road geometry, lane markings, and key landmarks, while discarding redundant detailed map data. This creates a sparse map that retains navigation functionality with dramatically reduced data storage requirements.
3Quantity of substance
If sparse map data is used for autonomous vehicle localization, then data storage requirements are reduced, but localization precision and measurement accuracy worsen
Solution Approach 1:
The patent applies local quality by storing high-precision data only where navigation-critical features are present (such as lane markings and road geometry at intersections) while using lower precision or no data in areas without navigation-relevant features. This selective precision maintains localization accuracy where needed while reducing overall data requirements.
Solution Approach 2:
The patent introduces intermediary reference landmarks and road geometry models that serve as mediators between the sparse map data and the vehicle's position estimation system. These intermediaries provide sufficient precision for localization without requiring dense map data, bridging the gap between sparsity and accuracy requirements.
Data Source
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AI summary
Systems and methods are provided for generating, distributing, and using a sparse map and lane measurements for autonomous vehicle navigation. For example, one implementation relates to a non-transitory computer-readable medium including a sparse map for autonomous vehicle navigation along a road segment. Another implementation relates to a method of generating a road navigation model for use in autonomous vehicle navigation. A third implementation relates to a system for autonomously navigating a vehicle along a road segment. A fourth implementation relates to a method of determining a line representation of a road surface feature extending along a road segment. A fifth implementation relates to a method of determining a line representation of a road surface feature extending along a road segment. A sixth implementation relates to a system for correcting a position of a vehicle navigating a road segment.