Multi-Vehicle Road Feature Alignment for Lightweight Navigation Maps
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amounts of data they need to process, analyze, and store, including image data, map data, GPS data, and sensor data, which can limit their navigation capabilities.
Innovation Solution
The use of cameras to provide vehicle navigation features, where one or more cameras monitor the environment of a vehicle, and a processor analyzes the images to detect indicators of intersections, stopping locations, and other vehicles, sending this information to a server for updating a road navigation model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use traditional mapping technology to navigate, then navigation capability is provided, but the volume of data needed to store and update the map becomes extremely large
Solution Approach 1:
The patent extracts only the essential navigation elements (road geometry, intersections, stop lines) from complete traditional maps. Instead of storing and processing entire map datasets, the system identifies and utilizes only the critical features needed for navigation decisions, dramatically reducing data volume while maintaining navigation reliability
Solution Approach 2:
The navigation system segments the road environment into discrete, manageable features such as intersections, stop lines, and road geometry elements. Each feature is processed and stored independently, allowing the system to handle navigation data in smaller, more efficient units rather than as a monolithic large-scale map
2Measurement precision
If autonomous vehicles process and interpret vast volumes of sensor data, then navigation accuracy is improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The system extracts only the relevant navigation features from sensor data streams. Instead of processing complete image datasets or full sensor outputs, the system identifies and processes only the essential elements (intersections, stop lines, road geometry), reducing computational complexity while preserving navigation accuracy
Solution Approach 2:
The system performs preliminary identification and classification of navigation features before detailed processing. By pre-identifying relevant features such as intersections and stop lines in the sensor data, the system prepares the data in advance for more efficient processing, reducing overall computational complexity
Data Source
AI summary
A system for correlating information collected from a plurality of vehicles relative to a common road segment is disclosed. The vehicle system includes at least one processor programmed to receive a first set of drive information from a first vehicle including first and second indicators of position associated with detected semantic and non-semantic road features; receive a second set of drive information from a second vehicle including third and fourth indicators of position associated with the detected semantic and non-semantic road features; correlate the first and second sets of drive information by determining a refined position of the detected semantic road feature based on the first and third indicators and a refined position of the detected non-semantic road feature based on the second and forth indicators; store the refined positions of the detected semantic and non-semantic road features in a map; and distribute the map to one or more vehicles.