Vehicle Offset Warning Using Steerable Filters
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Solution Overview
Problem
Existing lane identification technologies in ADAS systems are ineffective in changing road environments, leading to increased false warnings and false detections, which can pose safety risks during vehicle operation.
Innovation Solution
A method and apparatus for early warning of vehicle offset that involves acquiring and processing road images to determine target lane lines using grayscale processing and steerable filters, with threshold distance conditions to trigger warnings, thereby improving lane line identification accuracy and reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing lane identification technology is used in changing road environments, then the system can operate in various conditions, but the accuracy of lane line identification deteriorates leading to false warnings and false detections
Solution Approach 1:
The patent applies local quality by focusing the steerable filter operation on a specific near field-of-view region rather than processing the entire image. This localized approach concentrates computational resources on the most relevant area for lane detection, improving identification accuracy in changing environments while reducing false warnings. The near field-of-view region is specifically defined to capture critical lane line information where the vehicle will primarily operate.
Solution Approach 2:
The patent implements dynamics by using a steerable filter that can dynamically adjust its orientation and parameters based on the specific road conditions and lane line angles detected. This dynamic adaptability allows the system to maintain high identification accuracy across various changing road environments, effectively resolving the contradiction between versatility and precision by enabling the filter to optimize its performance for each specific scenario.
2Measurement precision
If comprehensive image processing is performed to improve lane line identification accuracy, then measurement precision improves, but device complexity and computational load increase
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into distinct stages: grayscale conversion, near field-of-view region extraction, steerable filter application, and lane line fitting. This segmented approach improves lane line identification accuracy by systematically addressing each processing step while managing device complexity through modular organization of processing functions.
Solution Approach 2:
The patent extracts only the necessary near field-of-view region from the complete road image before applying the steerable filter. This extraction principle reduces the amount of data requiring complex processing, thereby improving lane line identification accuracy with reduced computational load and lower device complexity requirements compared to processing the entire image.
3Device complexity
If traditional lane identification methods are used, then device complexity remains low, but false warnings and false detections increase reducing system reliability
Solution Approach 1:
The patent performs preliminary grayscale conversion and near field-of-view region extraction before applying the steerable filter for lane line detection. These preliminary actions prepare the data in advance, enabling more reliable lane line identification with reduced false warnings. The preprocessing steps organize and optimize the input data, improving warning system reliability while maintaining relatively low device complexity through efficient sequential processing.
Solution Approach 2:
The steerable filter acts as an intermediary between the raw image data and the final lane line detection results. This intermediary processing step enhances warning system reliability by providing a robust mechanism for extracting lane line features, reducing false detections while maintaining manageable device complexity through the use of established filter-based image processing techniques.
Data Source
AI summary
The present disclosure provides a method and apparatus for early warning of vehicle offset. The method includes: acquiring collected road image to be detected; obtaining a corresponding grayscale image by performing grayscale processing on the road image to be detected; determining a target lane line within the grayscale image; and determining whether to issue an early warning of vehicle offset according to the target lane line and an early warning trigger condition.