Layered Road Mapping From Multi-Vehicle Image Correlation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amounts of data they need to process and store, particularly with traditional mapping technologies, which can limit their ability to accurately identify locations, navigate through intersections, and avoid obstacles.
Innovation Solution
The use of cameras to analyze images and sensors to detect intersections, other vehicles, and road conditions, with data being sent to a server to update a road navigation model, allowing for real-time adjustments and improved navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to navigate autonomous vehicles, then comprehensive map data can be stored and accessed, 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 from traditional comprehensive maps, creating a sparse map that contains only critical information needed for autonomous vehicle navigation such as road geometry, intersections, and key landmarks, eliminating unnecessary data while maintaining navigation reliability
Solution Approach 2:
The patent segments the navigation data into hierarchical layers, separating essential navigation information from supplementary data, allowing the system to store and process only the necessary components for safe autonomous operation
2Measurement precision
If vast volumes of information are collected and analyzed by autonomous vehicles, then navigation decisions can be made based on comprehensive data, but the sheer quantity of data can limit or adversely affect autonomous navigation
Solution Approach 1:
The patent applies local quality by focusing computational resources on analyzing only the relevant portions of the environment captured by sensors, such as detecting and analyzing specific road features, intersections, and obstacles in the vehicle's immediate path rather than processing all captured data uniformly
Solution Approach 2:
The patent performs preliminary action by pre-processing sensor data to identify and extract only the most relevant features and information needed for navigation decisions, filtering out unnecessary data before it reaches the decision-making algorithms, thus speeding up processing while maintaining accuracy
3Measurement precision
If autonomous vehicles rely on comprehensive map data, then accurate location identification is possible, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent creates simplified copies of map data in the form of sparse maps that contain only the essential geometric and topological information needed for navigation, rather than storing and managing complete high-resolution map datasets, reducing data management complexity while maintaining location identification accuracy
Data Source
AI summary
A system may include a processor configured to receive a first image captured during a drive of a first vehicle along a road segment and receive a second image captured during a drive of the second vehicle along the road segment; analyze the first and second images to identify representations of objects; analyze the first and second images to determine position indicators for each of the objects relative to the road segment; correlate the position indicators for each of the objects, wherein the correlating includes determining refined positions of each object based on the determined position indicators; and generate, based on the refined positions of objects belonging to a particular predetermined category of objects, a map including representations of the refined positions of one or more of the objects that belong to the particular predetermined category of objects.