Anonymized Road Navigation Models With Sparse Map Segmentation
Find Innovative SolutionsGenerate Solutions
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 efficiently analyze and update maps while traveling.
Innovation Solution
The system employs cameras to collect and analyze environmental data, using a processor to determine motion representations and road characteristics, and a server-based system to generate and distribute a sparse map for navigation, reducing data storage and transfer requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used to navigate, then navigation accuracy is improved, but data storage and processing burden increases
Solution Approach 1:
The patent segments the continuous map data into discrete road segment information, where each segment contains only the essential navigation characteristics needed for autonomous vehicle operation. This segmentation allows the system to process and store only relevant portions of map data rather than complete high-resolution maps, reducing data volume while maintaining navigation accuracy.
Solution Approach 2:
The system extracts only the critical navigation features from traditional map data, such as road geometry, intersections, and key landmarks, while discarding redundant information. This extraction process creates a simplified representation that retains navigation functionality but significantly reduces the data storage and processing requirements.
2Reliability
If complete map data is stored and updated, then navigation reliability is improved, but system complexity and update burden increase
Solution Approach 1:
The navigation system divides the map into discrete road segments that can be independently processed and updated. Each segment contains specific navigation information that can be validated and confirmed individually, improving reliability through modular verification while reducing system complexity through manageable units of data.
Solution Approach 2:
The system employs autonomous confirmation processes where the navigation system automatically validates and confirms road segment information without requiring manual intervention for every update. This self-service approach maintains navigation reliability through continuous verification while reducing the operational complexity of map management.
3Loss of information
If all sensor data is transmitted to server, then data completeness is improved, but data transfer efficiency deteriorates
Solution Approach 1:
The system extracts and transmits only the essential navigation data and confirmed road segment information to the server, filtering out redundant sensor data that does not contribute to navigation accuracy. This selective extraction maintains data completeness for navigation purposes while significantly improving data transfer efficiency by reducing bandwidth requirements.
Solution Approach 2:
The system transmits partial data sets that contain only the critical information needed for navigation, rather than transmitting complete sensor data sets. This partial action approach ensures that the most important navigation information is communicated efficiently, achieving sufficient data completeness without the overhead of transmitting all available sensor data.
Data Source
AI summary
Systems and methods are provided for collecting anonymized drive information. A processing device may be configured to receive navigation information associated with a common road section including first road segment information relative to a first portion of the common road section, second road segment information relative to a second portion of the common road section, and third road segment information relative to the third portion of the common road section; store the navigation information associated with the common road section; generate at least a portion of an autonomous vehicle road navigation model for the common road section based on the navigation information; and distribute the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road section.