Sparse Road Segment Modeling for Autonomous Navigation Maps
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating efficiently due to the vast amounts of data required for processing and storing information from various sources, such as cameras, GPS, and sensors, which can lead to limitations in navigation accuracy and data management.
Innovation Solution
A system and method for correlating drive information from multiple road segments to generate sparse navigational maps, where a processor receives and correlates drive information to create road models, and stores these models in a sparse map for efficient navigation, reducing the need for extensive data storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to store and update map data, then navigation coverage is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete map data to create sparse maps. Instead of storing and processing all available map information, the system identifies and retains only critical features such as road boundaries, intersections, and key landmarks that are necessary for navigation decisions, thereby reducing data volume while maintaining navigation reliability
Solution Approach 2:
The patent segments the continuous map data into discrete road segments and further divides them into smaller navigational units. Each segment is processed independently to identify essential features, allowing the system to manage large-scale navigation data through modular processing and storage of only the most important segment characteristics
2Measurement precision
If complete map data is stored and processed, then navigation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential geometric and topological features from complete map data that are necessary for accurate location determination. By removing redundant information and retaining only critical navigational elements, the processing time is significantly reduced while location accuracy is maintained through the preservation of key spatial relationships
Solution Approach 2:
The patent applies partial action by processing only the portions of map data that are essential for navigation rather than analyzing the complete dataset. The system identifies and processes only the minimum necessary features required for accurate positioning and route planning, avoiding the computational overhead of processing unnecessary data
3Adaptability or versatility
If vast volumes of data are collected and analyzed, then navigation completeness is improved, but system complexity and data management burden increase
Solution Approach 1:
The patent extracts essential navigational information from vast volumes of collected data, identifying and retaining only the features that contribute to navigation completeness. This extraction process filters out redundant and non-essential data, reducing the data management burden while preserving the adaptability needed for various navigation scenarios
Solution Approach 2:
The system segments navigation data into organized categories such as road segments, intersections, landmarks, and connectivity relationships. This segmentation allows for structured storage and management of navigation information, making the system more adaptable to different navigation needs while reducing the overall complexity of data management through systematic organization
Data Source
AI summary
A system for correlating drive information from multiple road segments is disclosed. In one embodiment, the system includes memory and a processor configured to receive drive information from vehicles that traversed a first road segment and vehicles that traversed a second road segment. The processor is configured to correlate the drive information from the vehicles to provide a first road model segment representative of the first road segment and a second road model segment representative of the second road segment. The processor correlates the first road model segment with the second road model segment to provide a correlated road segment model if a drivable distance between a first point associated with the first road segment and a second point associated with the second road segment is less than or equal to a predetermined distance threshold, and stores the correlated road segment model as part of a sparse navigational map.