Lane Marking Model Refinement Using Camera Odometry
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately navigating roadways, particularly in recognizing and responding to lane markings, especially under adverse conditions such as low visibility or damaged markings, due to limitations in image processing and detection algorithms.
Innovation Solution
The implementation of a system that uses multiple cameras to monitor the vehicle's environment, incorporating top-down refinement techniques to update and refine lane marking models based on camera odometry, appearance, and spacing between dashes, enabling more accurate navigational responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing and detection algorithms are used for lane marking recognition, then the system is simpler to implement, but the detection accuracy deteriorates under adverse conditions such as low visibility or damaged markings
Solution Approach 1:
The patent segments the lane marking detection task into multiple components: initial lane marking detection, top-down refinement using camera odometry, and model updating based on appearance and spacing features. This segmentation allows each component to be optimized independently, improving overall detection accuracy while managing system complexity through modular processing stages.
Solution Approach 2:
The system performs preliminary actions by pre-establishing a lane marking model that is continuously refined using top-down constraints from camera odometry and historical data. This preliminary model provides a robust foundation that improves detection accuracy under adverse conditions before final lane marking recognition occurs.
2Reliability
If multiple cameras and refinement techniques are implemented, then lane marking detection accuracy improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the lane marking model is continuously updated based on detected lane markings, camera odometry data, and refinement results. This closed-loop feedback system improves detection reliability by constantly adapting the model to current conditions while using the same camera system for both detection and model updating, avoiding the need for additional sensors.
Solution Approach 2:
The system performs self-service by using its own camera odometry and detected lane marking data to refine and update its internal model. The same camera system that detects lane markings also provides the odometry information needed for top-down refinement, eliminating the need for separate sensing systems and reducing overall device complexity.
3Measurement precision
If top-down refinement based on camera odometry is used, then the precision of lane marking model updates improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies partial refinement actions by selectively updating only those portions of the lane marking model that require refinement based on current detection uncertainty or adverse conditions. Rather than continuously refining the entire model at maximum precision, the system applies refinement selectively, reducing processing time while maintaining necessary model accuracy.
Data Source
AI summary
Systems and methods use cameras to provide autonomous navigation features. In one implementation, top-down refinement in lane marking navigation is provided. The system may include one or more memories storing instructions and one or more processors configured to execute the instructions to cause the system to receive from one or more cameras one or more images of a roadway in a vicinity of a vehicle, the roadway comprising a lane marking comprising a dashed line, update a model of the lane marking based on odometry of the one or more cameras relative to the roadway, refine the updated model of the lane marking based on an appearance of dashes derived from the received one or more images and a spacing between dashes derived from the received one or more images, and cause one or more navigational responses in the vehicle based on the refinement of the updated model.