Landmark Orientation Mapping for Low-Data Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data required for processing, storage, and updating maps, especially when relying on traditional mapping technologies.
Innovation Solution
The system aggregates drive information from multiple vehicles to identify landmark clusters and determine location identifiers, which are then stored in a map and distributed to autonomous vehicles for navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for autonomous vehicle navigation, then navigation accuracy is maintained, but the volume of data required for processing, storage, and updating maps increases significantly
Solution Approach 1:
The patent extracts and utilizes only the essential navigational elements (landmarks, road geometry, signage) from the environment rather than processing complete traditional maps. By identifying and storing only critical location identifiers and their spatial relationships, the system achieves accurate navigation with significantly reduced data requirements.
Solution Approach 2:
The patent segments the navigation problem into discrete landmark identification and location determination tasks. Instead of processing continuous map data, the system divides the environment into identifiable landmarks and uses their relative positions to determine vehicle location, reducing overall data volume while maintaining navigational accuracy.
2Adaptability or versatility
If complete map data is stored and updated for autonomous navigation, then comprehensive route information is available, but the complexity of data management and processing increases
Solution Approach 1:
The system performs preliminary identification and classification of landmarks during the mapping phase, organizing data by location identifiers before actual navigation occurs. This pre-processing structures the data in a way that simplifies subsequent query and route planning operations, reducing real-time processing complexity.
Solution Approach 2:
Instead of storing and managing complete traditional map datasets, the system creates simplified representations (copies) that capture only the essential navigational features - landmark locations, identifiers, and spatial relationships - sufficient for autonomous navigation without the complexity of full map data management.
Data Source
AI summary
Systems and methods may detect objects. In one implementation, a method may include receiving drive information obtained by a plurality of vehicles traversing or having traversed a road segment. The drive information may include landmark detection information corresponding to a landmark located along the road segment and including at least a first location indicator associated with the landmark and a second location indicator associated with the landmark. The method may include aggregating the drive information to determine a refined first location indicator and a refined second location indicator associated with the landmark. The method may include determining a landmark orientation for at least one feature of the landmark based on the refined first location indicator and the refined second location indicator and storing the landmark orientation in a map. The method may include distributing the map to one or more autonomous vehicles for use in navigating along the road segment.