Vision-Based Flashing Light Detection for Emergency Vehicle Response
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately tracing and detecting objects, particularly emergency vehicles, across multiple images due to position errors and changes in vehicle position, which affects navigation decisions.
Innovation Solution
The implementation of a polar grid system that partitions the image region into polar bins, allowing for accurate detection and tracing of objects by comparing image content across images, identifying changes in color and brightness, and determining the type of vehicles based on flashing light signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection methods are used to trace vehicles across multiple images, then the system can detect objects, but position errors accumulate and detection accuracy deteriorates due to changes in vehicle position
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) based on detected vehicle positions. Each ROI is processed independently to detect flashing light signals, allowing the system to maintain high detection accuracy even when vehicles move between frames. This segmentation approach prevents error propagation that would occur with global image processing methods.
Solution Approach 2:
The system dynamically adjusts detection parameters such as ROI size, flash detection thresholds, and temporal filtering based on vehicle motion characteristics and image conditions. By changing these parameters adaptively, the system maintains reliable tracking accuracy across multiple images despite variations in vehicle position, speed, and lighting conditions.
2Measurement precision
If the system processes multiple images to trace vehicles and detect flashing lights, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the image processing task into segmented regions of interest around detected vehicles. Instead of analyzing entire images or all pixels, the system focuses computational resources only on relevant ROIs where flashing lights are likely to occur. This segmentation dramatically reduces computational complexity while maintaining high detection accuracy for the target signals.
Solution Approach 2:
The system extracts and isolates specific features (flashing light signals) from the complex image data by comparing temporal variations in ROI pixel values. By extracting only the relevant temporal frequency information and discarding static or irrelevant image content, the system achieves accurate flash detection with reduced computational burden compared to full-image analysis.
3Measurement precision
If the system uses detailed image analysis to determine vehicle types and light signal types, then classification accuracy improves, but processing speed decreases
Solution Approach 1:
The patent extracts only the essential features needed for classification: the temporal pattern of flashing lights, color information, and position data. By extracting these key discriminative features and ignoring redundant image details, the system achieves accurate vehicle and signal type classification while maintaining high processing speed for real-time navigation decisions.
Solution Approach 2:
The system performs preliminary classification based on flashing light characteristics (color, flash pattern) before conducting more detailed analysis. This hierarchical approach allows quick initial classification to guide navigation decisions, with more detailed image analysis performed only when necessary, thereby balancing classification accuracy with processing speed.
Data Source
AI summary
A computing device of a first vehicle may receive a first image and a second image of a second vehicle having flashing light signals. The computing device may determine, in the first image and the second image, an image region that bounds the second vehicle such that the image region substantially encompasses the second vehicle. The computing device may determine a polar grid that partitions the image region in the first image and the second image into polar bins, and identify portions of image data exhibiting a change in color and a change in brightness between the first image and the second image. The computing device may determine a type of the flashing light signals and a type of the second vehicle; and accordingly provide instructions to control the first vehicle.