Autonomous Vehicle Lane Boundary Detection for Real-Time Accuracy
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Solution Overview
Problem
Conventional systems for detecting lanes and road boundaries in autonomous vehicles are either too computationally expensive to run in real-time or suffer from inaccuracy due to shortcuts implemented to reduce computing requirements, limiting their ability to operate safely in all road and weather conditions.
Innovation Solution
The use of a deep neural network trained with low-resolution images, region of interest images, and various ground truth masks enables accurate real-time detection of lanes and road boundaries, even in less than ideal weather and road conditions, while being computationally inexpensive.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision techniques are used to detect lanes and road boundaries, then measurement precision is improved, but productivity deteriorates due to high computational cost preventing real-time operation
Solution Approach 1:
The patent segments the image processing task by dividing the image into multiple scales (e.g., different resolution levels) and processing each scale separately. This allows the system to capture both fine details and broader contextual information efficiently, improving detection accuracy while reducing overall computational burden compared to processing the entire high-resolution image at once.
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting only the most critical features (such as lane boundaries and road edges) rather than attempting to identify all objects in the scene. This selective approach maintains real-time processing capability while achieving sufficient detection accuracy for safe autonomous vehicle operation.
2Productivity
If computational shortcuts are implemented to enable real-time operation, then productivity is improved, but measurement precision deteriorates due to reduced detection accuracy
Solution Approach 1:
The patent segments the detection task into multiple scales where coarse-level detection identifies potential regions of interest quickly, and fine-level detection refines the results in those specific regions. This hierarchical segmentation enables real-time processing while maintaining high detection accuracy by applying detailed analysis only where needed.
Solution Approach 2:
The patent introduces a scale dimension by processing images at multiple resolutions simultaneously. This allows the system to leverage both low-resolution images for fast initial detection and high-resolution images for detailed verification, achieving real-time performance without sacrificing accuracy.
3Measurement precision
If conventional systems operate in ideal conditions, then measurement precision is maintained, but adaptability deteriorates as systems fail in adverse weather and road conditions
Solution Approach 1:
The patent designs a multi-scale detection system that functions effectively across diverse conditions by capturing both fine details and broader contextual patterns. This universal approach allows the same system to maintain detection accuracy in varying weather conditions, lighting scenarios, and road types without requiring condition-specific adjustments.
Solution Approach 2:
By operating across multiple scale dimensions, the system gains robustness to environmental variations. Low-resolution scales provide stable detection under poor lighting or adverse weather, while high-resolution scales enable accurate detection in ideal conditions, allowing the system to adapt to varying environmental quality automatically.
Data Source
AI summary
In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.