Dynamic Condition Reporting for Low-Data Vehicle Navigation
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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 features, 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 navigation data is available, but the volume of data to store and update becomes unmanageably large
Solution Approach 1:
The patent extracts only the essential navigation elements (intersections, stop lines, lane markings) from the complete map data, storing them as discrete features rather than comprehensive imagery. This reduces data volume while maintaining navigation reliability by focusing on critical decision-making elements.
Solution Approach 2:
The map is segmented into discrete navigational features (intersections, stop lines, lane markings) that can be independently identified, stored, and processed. This segmentation allows the system to handle only relevant data portions rather than entire map datasets, reducing storage requirements while preserving navigation accuracy.
2Reliability
If vast volumes of data are collected and processed by the autonomous vehicle, then navigation decisions can be made with comprehensive information, but the processing complexity and time requirements increase significantly
Solution Approach 1:
The system extracts only critical navigational features (intersections, stop lines, lane markings) from the environment, ignoring irrelevant data. This extraction approach maintains decision accuracy by focusing on essential elements while dramatically reducing processing complexity compared to analyzing complete environmental datasets.
Solution Approach 2:
The server performs preliminary processing of map data before transmission to the vehicle, pre-identifying and structuring navigational features. This preliminary action reduces the processing burden on the vehicle by delivering ready-to-use navigational information rather than raw data requiring extensive analysis.
3Adaptability or versatility
If complete map data is stored locally in the autonomous vehicle, then navigation can proceed without external connectivity, but the storage requirements and update challenges become prohibitive
Solution Approach 1:
The system stores only extracted navigational features (intersections, stop lines, lane markings) rather than complete map data. This extraction enables offline navigation capability with minimal storage requirements, as only essential decision-making elements are retained locally while maintaining the ability to update via connectivity when available.
Data Source
AI summary
A system may include at least one processor including circuitry and a memory. The memory may include instructions executable by the circuitry to cause the at least one processor programmed to receive at least one identifier associated with a condition having at least one dynamic characteristic. The at least one identifier may be determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle, and analysis of the at least one image to identify the condition in the environment, and analysis of the at least one image to determine the at least one identifier associated with the condition. The at least one processor may also be programmed to update a database record to include the at least one identifier associated with the condition, and distribute the database record to at least one entity.