Road Navigation Model Updates Using Non-Semantic Feature Points
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process and store, including visual information, GPS data, and sensor data, which can limit their navigation capabilities and require significant updates to traditional mapping technologies.
Innovation Solution
The system uses cameras to analyze images and sensors to detect intersections, other vehicles, and road features, sending this information to a server to update a road navigation model, allowing for efficient navigation and map generation without the need for extensive data storage or updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to navigate autonomous vehicles, then navigation capability is provided, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the essential navigation elements (road geometry, intersections, stop lines) from the environment using computer vision, rather than storing comprehensive map data. This selective extraction reduces data volume while maintaining navigation reliability by focusing on critical features needed for autonomous driving decisions
Solution Approach 2:
The navigation system segments the road environment into discrete, identifiable features such as intersections, stop lines, and road geometry. By processing and storing only these segmented elements rather than continuous map data, the system reduces overall data requirements while preserving essential navigation information
2Measurement precision
If vast volumes of information are collected and stored for autonomous navigation, then navigation accuracy is improved, but the challenges limit or adversely affect autonomous navigation
Solution Approach 1:
The patent replaces traditional mechanical mapping systems (which require extensive data storage and processing infrastructure) with computer vision-based perception systems. By using cameras and image processing algorithms to directly perceive road features, the system achieves navigation accuracy without the complexity of managing vast data volumes
Solution Approach 2:
The autonomous vehicle uses its own camera system to perceive and interpret road features in real-time, rather than relying on pre-stored map data. This self-service approach allows the vehicle to navigate accurately by independently identifying intersections, stop lines, and road geometry, reducing system complexity
3Productivity
If real-time navigation model updates are implemented, then navigation efficiency is improved, but data processing requirements increase
Solution Approach 1:
The system performs partial updates to the navigation model by only processing and transmitting changes in road features (such as newly detected intersections or modified stop lines) rather than continuously updating the entire map. This selective updating approach improves navigation efficiency while reducing data processing energy requirements
Data Source
AI summary
A navigation system may include a processor programmed to analyze a first image to identify a non-semantic road feature; identify a first image location, in the first image, of one point associated with the non-semantic road feature; analyze a second image to identify a representation of the non-semantic road feature in the second image; identify a second image location, in the second image, of the one point associated with the non-semantic road feature; determine, based on a difference between the first and second image locations and based on motion information for a vehicle between a capture of the first image and a capture of the second image, three-dimensional coordinates for the one point associated with the non-semantic road feature; and send the three-dimensional coordinates for the one point associated with the non-semantic road feature to a server for use in updating a road navigation model.