Lane Arrow Mapping for Low-Complexity 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, particularly with traditional mapping technologies, which can limit their ability to efficiently analyze and update maps, leading to difficulties in identifying lane marks, obstacles, and navigating through intersections.
Innovation Solution
The use of cameras and processors to analyze images and update autonomous vehicle road navigation models, including lane marks, directional arrows, traffic lights, and free spaces, allowing for real-time navigation and data distribution among vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to store and update map data, then comprehensive map coverage is achieved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigation elements (lane marks, directional arrows, traffic lights, free spaces) from complete map data, storing only these critical features rather than entire map images. This reduces data storage requirements and processing complexity while maintaining sufficient accuracy for autonomous navigation.
Solution Approach 2:
The patent segments map data into discrete, identifiable elements (lane marks, directional arrows, traffic lights, free spaces) rather than storing continuous map images. Each element is independently detected, stored with location identifiers, and processed, which simplifies data management and reduces overall system complexity.
2Measurement precision
If vast volumes of image data, map data, GPS data, and sensor data are collected and analyzed, then navigation accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary detection and identification of navigation elements (lane marks, directional arrows, traffic lights) during the data collection phase, organizing them with location identifiers before actual navigation decisions are made. This pre-processing reduces the computational burden during real-time navigation, decreasing data processing time while maintaining accuracy.
Solution Approach 2:
The system extracts only the essential navigation elements (lane marks, directional arrows, traffic lights, free spaces) from complete map data, storing only these critical features rather than entire map images. This reduces data storage requirements and processing complexity while maintaining sufficient accuracy for autonomous navigation.
3Manufacturing precision
If detailed lane mark detection and mapping is performed, then navigation precision is improved, but the complexity of detecting and measuring increases
Solution Approach 1:
The patent segments lane mark detection into distinct, manageable tasks: detecting lane marks, detecting directional arrows, detecting traffic lights, and identifying free spaces. Each element is detected and stored with location identifiers separately, which simplifies the overall detection process while maintaining high mapping precision for each element type.
Data Source
AI summary
A system for mapping a lane mark for use in autonomous vehicle navigation is provided. The system includes at least one processor programmed to: receive two or more location identifiers associated with a detected lane mark; associate the detected lane mark with a corresponding road segment; update an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane mark; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.