Emergency Vehicle Warning Light Recognition Across Video Frames
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Solution Overview
Problem
Autonomous vehicles face challenges in quickly and accurately recognizing emergency vehicles equipped with warning lights, which is crucial for safe driving control, especially when emergency vehicles need to proceed ahead of others.
Innovation Solution
A method and system that utilize a combination of deep learning-based and computer vision-based processes to recognize the state of warning lights in emergency vehicles by extracting warning light areas, performing RGB-to-HSV conversion, and determining the on/off state based on pixel saturation and channel values, with results accumulated across consecutive frames to ensure accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single object recognition process is used to detect emergency vehicles, then the device complexity is low, but the recognition accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple recognition approaches (deep learning-based recognition and computer vision-based recognition) into a unified system. The controller performs both deep learning analysis and computer vision analysis on warning light images, then integrates the results to determine the final warning light state, thereby improving reliability through multi-method verification
Solution Approach 2:
The patent introduces an intermediary accumulation mechanism that collects recognition results from multiple consecutive frames before making a final determination. This intermediary step filters out transient errors and confirms consistent detection patterns, enhancing recognition reliability without requiring immediate single-frame accuracy
2Measurement precision
If deep learning-based recognition is used for warning light detection, then the recognition accuracy is high, but the processing time increases
Solution Approach 1:
The system performs preliminary object recognition to identify the warning light area in the image before conducting detailed deep learning-based state analysis. This preliminary step localizes the region of interest, allowing subsequent deep learning processing to focus only on relevant areas rather than entire images, thereby reducing overall processing time while maintaining high accuracy
Solution Approach 2:
The patent segments the image processing task into distinct stages: first identifying the warning light area through object recognition, then analyzing the state of the warning light within that specific region using deep learning. This segmentation allows optimized processing at each stage, improving efficiency without sacrificing measurement precision
3Reliability
If multiple consecutive frames are analyzed for warning light state recognition, then the recognition reliability is improved, but the loss of time increases
Solution Approach 1:
The system implements a feedback mechanism where recognition results from previous frames are accumulated and used to inform current frame analysis. The controller maintains a record of warning light states across multiple frames and uses this historical data to confirm or correct current detections, creating a self-reinforcing system that improves reliability while managing processing time through intelligent frame selection
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid and accurate recognition of emergency vehicles' warning light states, facilitating safe autonomous driving controls by providing reliable data for vehicle path adjustments or temporary stops when necessary.
Implementation Method 1
a camera configured to capture a video including a plurality of image frames associated with surroundings of the vehicle
Data Source
AI summary
Vehicle and an object recognition method of the vehicle are disclosed. An object recognition method of a vehicle includes performing an object recognition of an emergency vehicle equipped with a warning light in a video recorded around the vehicle, performing a first warning light state recognition of the warning light by using a single frame of the video, performing a second warning light state recognition of the warning light by using a single frame of the video, and performing a third warning light state recognition based on a first result of the first warning light state recognition and a second result of the second warning light state recognition in a plurality of consecutive frames of the video.