Traffic Sign Landmark Mapping for Sparse AV Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data required for traditional mapping technologies, which can limit their ability to efficiently process and store information from various sensors and cameras, making it difficult to accurately navigate roadways and handle complex environments.
Innovation Solution
The implementation of a sparse map system that uses cameras and other sensors to provide navigation features, including a polynomial representation of target trajectories and landmarks, allowing for efficient data storage and adaptive navigation, with data density optimized to maintain longitudinal position accuracy within 1 meter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used for autonomous vehicle navigation, then navigation accuracy is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical landmarks, road segments, and connectivity information needed for navigation. This extraction principle reduces data storage requirements while maintaining sufficient navigation accuracy by eliminating redundant map data.
Solution Approach 2:
The patent segments the navigation problem into discrete components: landmarks, road segments, and connectivity relationships. By representing the environment as a graph structure with nodes (landmarks) and edges (road segments), the system achieves efficient data storage and processing while maintaining navigation accuracy through structured representation.
2Reliability
If complete map data is stored for autonomous navigation, then navigation reliability is improved, but system complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical landmarks, road segments, and connectivity information needed for navigation. This extraction principle reduces data storage requirements while maintaining sufficient navigation accuracy by eliminating redundant map data.
Solution Approach 2:
The patent segments the navigation problem into discrete components: landmarks, road segments, and connectivity relationships. By representing the environment as a graph structure with nodes (landmarks) and edges (road segments), the system achieves efficient data storage and processing while maintaining navigation accuracy through structured representation.
3Measurement precision
If high-density map data is used, then position accuracy is improved, but data transmission and processing efficiency decrease
Solution Approach 1:
The patent extracts only the essential navigational elements from complete maps, creating sparse maps that contain only critical landmarks, road segments, and connectivity information needed for navigation. This extraction principle reduces data storage requirements while maintaining sufficient navigation accuracy by eliminating redundant map data.
Solution Approach 2:
The patent changes the representation parameters from continuous high-resolution map data to discrete graph structure parameters (nodes, edges, connectivity). This parameter transformation enables efficient data storage and processing while maintaining position accuracy through the structured graph representation and landmark-based positioning.
Data Source
AI summary
Systems and methods are disclosed for identifying landmarks. A method for identifying a landmark may include initiating identification of a landmark based on one or more images from a camera, for use in autonomous vehicle navigation, the landmark including a traffic sign; initiating updating a road model with a location of the landmark; and initiating distribution of the road model with the location of the traffic sign to a plurality of autonomous vehicles.