Lane Boundary Detection Using ROI Segmentation for Real-Time Driving
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Solution Overview
Problem
Conventional systems for detecting lane and road boundaries by autonomous vehicles are either too computationally expensive to operate in real-time or suffer from inaccuracy due to shortcuts in processing requirements, limiting their ability to safely navigate in various road and weather conditions.
Innovation Solution
The use of a deep neural network trained with low-resolution images, region of interest images, and ground truth masks to detect lanes and boundaries, which is computationally inexpensive and capable of real-time deployment, allowing for accurate detection in diverse conditions through post-processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision techniques are used for lane detection, 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 road scene into multiple regions of interest (ROI), focusing computational resources only on areas where lane markings are likely to appear. This segmentation approach reduces the overall computational burden while maintaining detection accuracy in critical areas.
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. High-resolution processing is applied to ROIs where lane markings are expected, while lower resolution or no processing is applied to irrelevant areas. This local quality differentiation optimizes the balance between detection precision and processing speed.
2Productivity
If computational shortcuts are implemented to achieve real-time operation, then productivity is improved, but measurement precision deteriorates due to reduced detection accuracy
Solution Approach 1:
The patent performs preliminary actions by pre-identifying regions of interest based on basic image features before applying detailed lane detection algorithms. This preliminary segmentation prepares the data structure and identifies critical areas in advance, enabling faster subsequent processing without sacrificing accuracy in the identified ROIs.
Solution Approach 2:
The patent applies partial action by focusing computational effort only on specific regions of the image where lane markings are likely to be present, rather than processing the entire image uniformly. This selective application of processing power achieves real-time performance while maintaining accuracy where it matters most.
3Ease of operation
If conventional detection systems are used, then ease of operation is maintained, but reliability deteriorates in less than ideal road and weather conditions
Solution Approach 1:
The patent changes processing parameters dynamically based on detected conditions. When adverse weather or road conditions are detected, the system adjusts parameters such as ROI selection criteria, processing resolution, and detection thresholds to maintain reliability. This adaptive parameter adjustment allows the system to maintain both ease of operation and reliability across varying conditions.
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.