Lane Marker Detection Using High Dynamic Range Imaging
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Solution Overview
Problem
Existing lane departure warning systems face challenges in accurately differentiating lane markers from road noise, especially in situations with limited data points and varying road conditions, leading to potential false detections and decreased accuracy.
Innovation Solution
A system utilizing an imager and processor to capture high dynamic range images, detect lane markers, and apply heuristic methods and filtering techniques to accurately classify dashed lane markers by measuring dash start and end points over multiple frames, reducing error and enhancing signal-to-noise ratio for improved lane tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane detection methods are used, then the system can operate with simpler processing, but the accuracy of differentiating lane markers from road noise deteriorates
Solution Approach 1:
The patent segments the lane detection process into multiple distinct stages: image capture, preprocessing, lane marker detection, classification, and tracking. Each stage processes specific features independently, allowing complex analysis to be broken down into manageable steps that improve accuracy without overwhelming system complexity
Solution Approach 2:
The system performs preliminary actions by capturing multiple frames and pre-processing images before actual lane marker detection. High dynamic range images are captured and prepared in advance, with heuristic methods applied to identify potential lane markers before final classification, reducing the complexity of the main detection task
2Reliability
If multiple frames are processed to improve detection accuracy, then lane marker identification improves, but the processing time increases
Solution Approach 1:
The system employs periodic action by processing images at specific frame intervals rather than continuously analyzing every frame. Lane markers are detected and tracked periodically across multiple frames, allowing the system to accumulate reliability data while maintaining efficient processing timing
Solution Approach 2:
The system uses feedback mechanisms where detection results from previous frames inform subsequent processing. Tracking algorithms use historical data to predict current lane marker positions, reducing the processing required for each new frame while maintaining high detection reliability through cumulative learning
3Measurement precision
If heuristic methods and filtering techniques are applied, then false detections are reduced, but the computational load increases
Solution Approach 1:
The system applies partial filtering techniques that process only the most critical image regions and features. Heuristic methods are applied selectively to identify and classify lane markers, applying computational energy only where needed to reduce false detections without processing the entire image at full complexity
Solution Approach 2:
The system dynamically adjusts processing parameters based on road conditions and detection confidence levels. Filtering thresholds and heuristic criteria are modified according to the specific situation, reducing computational energy when conditions are favorable and increasing processing only when necessary to maintain detection accuracy
Data Source
AI summary
A system and method are disclosed for determining the presence and period of dashed line lane markers in a roadway. The system includes an imager configured to capture a plurality of high dynamic range images exterior of the vehicle and a processor, in communication with the at least one imager such that the processor is configured to process at least one high dynamic range image. The period of the dashed lane markers in the image is calculated for detecting the presence of the dashed lane marker and for tracking the vehicle within the markers. The processor communicates an output for use by the vehicle for use in lane departure warning (LDW) and/or other driver assist features.