Lane Mark Mapping Using Road-Segment Updates for AV Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data required for processing and storing information from various sources, such as cameras, GPS, and sensors, which can limit their navigation capabilities and pose daunting challenges in updating traditional mapping technologies.
Innovation Solution
The use of cameras to provide autonomous vehicle navigation features, including systems and methods that analyze images to detect lane marks, directional arrows, traffic lights, and free spaces, updating navigation models, and distributing these updates to multiple vehicles, allowing for real-time navigation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used to navigate autonomous vehicles, then navigation capability is provided, but the volume of data required to store and update the map becomes excessively large
Solution Approach 1:
The patent segments the navigation system into two parts: a compact pre-stored map containing only essential road topology and intersection information, and real-time visual data captured by onboard cameras. This segmentation allows the system to navigate using minimal stored data while relying on real-time image processing for detailed lane marking recognition and positioning, thereby reducing overall data storage requirements while maintaining navigation reliability
Solution Approach 2:
The system performs preliminary actions by pre-storing only critical road structure information (topology and intersections) in the map database before the vehicle reaches those locations. When the vehicle approaches pre-stored intersections or road segments, the system activates image capture and processing to recognize lane markings and confirm positioning, eliminating the need to store complete detailed map data for all road segments
2Measurement precision
If vast volumes of data are collected and processed by the autonomous vehicle, then navigation accuracy is improved, but the design challenges and processing burden increase
Solution Approach 1:
The patent extracts and isolates the most critical visual features (lane markings, directional arrows, traffic signals) from the complete environmental scene captured by cameras. By focusing processing only on these extracted features rather than analyzing all visual data, the system achieves accurate lane positioning and navigation while significantly reducing computational complexity and processing burden
Solution Approach 2:
The onboard camera system serves multiple functions: it captures images for lane marking recognition, identifies road signs and traffic signals, monitors surrounding environment for safety, and provides data for both positioning and navigation decisions. This multi-functionality reduces the need for separate specialized sensors and processing systems, thereby reducing overall device complexity while maintaining high navigation accuracy
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.