Traffic Light Cycle Mapping for Low-Data Autonomous Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amount of data they need to process and store, including image data, map data, and sensor data, which can limit their navigation capabilities and pose storage and update challenges for traditional mapping technologies.
Innovation Solution
The system utilizes cameras to analyze images for autonomous vehicle navigation, processing data to identify lane marks, directional arrows, traffic lights, and free spaces, and updates a navigation model based on this information, which is then distributed to multiple vehicles for improved navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to store and update map data for autonomous vehicles, then navigation capability is provided, but the sheer volume of data needed to store and update the map poses daunting challenges
Solution Approach 1:
The patent extracts only the essential navigation elements (lane marks, traffic lights, directional arrows, free spaces) from the complete map data, storing only what is necessary for autonomous vehicle navigation rather than the full traditional map dataset
Solution Approach 2:
The navigation model is segmented into discrete detectable elements (lane marks, traffic lights, directional arrows, free spaces) that can be independently identified and stored, allowing for more efficient data management and updates
2Measurement precision
If vast volumes of data are collected and processed by autonomous vehicles for navigation, then navigation accuracy is improved, but the sheer quantity of data to analyze, access, and store limits or adversely affects autonomous navigation
Solution Approach 1:
The system extracts only the critical navigation elements needed for autonomous driving (lane marks, traffic lights, directional arrows, free spaces) rather than processing all available data, reducing computational complexity while maintaining navigation accuracy
Solution Approach 2:
The navigation model is preprocessed and structured into identifiable elements before being used by autonomous vehicles, allowing for more efficient real-time processing and analysis during actual navigation
Data Source
AI summary
Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.