Traffic Sign Attribute Recognition for Unfamiliar Road Signs
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Solution Overview
Problem
Autonomous vehicles face challenges in responding to traffic signs, especially unfamiliar ones, as existing systems rely on pre-mapped data which can be outdated or inaccurate, leading to potential safety issues and travel delays.
Innovation Solution
The method involves real-time detection of traffic signs using sensors and image processing techniques to determine attributes such as type, color, and content, allowing the vehicle to respond appropriately without prior identification in the map information, and prioritizing assistance requests based on sign type for safe operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-mapped traffic sign data is used for identification, then the system can quickly recognize familiar signs, but it fails to identify unfamiliar or updated signs leading to safety issues
Solution Approach 1:
The system performs preliminary actions by capturing images of traffic signs with cameras before needing to identify them. This allows the system to have the sign image available for later analysis by the machine learning model, enabling identification of unfamiliar signs without requiring pre-mapped data.
Solution Approach 2:
A machine learning model acts as an intermediary between the captured traffic sign image and the identification result. The model receives the image, analyzes it to determine sign type and attributes, and outputs the identification, bridging the gap between raw visual data and meaningful interpretation for unfamiliar signs.
2Adaptability or versatility
If real-time image processing is performed to identify unfamiliar signs, then the vehicle can respond to unknown signs, but the processing time and computational resources increase
Solution Approach 1:
The system captures traffic sign images in advance using cameras as the vehicle approaches, storing them for immediate processing when needed. This preliminary capture action eliminates delays that would occur from attempting to process or request images in real-time when identification is required.
Solution Approach 2:
The system creates a digital copy of the traffic sign image through camera capture and stores it for processing. This copy can be immediately analyzed by the machine learning model without requiring physical interaction with the sign or real-time communication with external systems, significantly reducing identification time.
3Adaptability or versatility
If a machine learning model is used to determine sign type, then the system can identify unfamiliar signs, but it may produce false positives or negatives reducing accuracy
Solution Approach 1:
The system implements feedback by comparing the machine learning model's identification results against the actual captured image and known traffic sign characteristics. When discrepancies or low confidence results occur, the system can request additional images, adjust processing parameters, or flag for manual review, continuously improving accuracy through feedback loops.
Solution Approach 2:
The system performs preliminary verification by capturing multiple images or additional views of the traffic sign before final identification. This preliminary data collection allows the machine learning model to work with more comprehensive information, reducing false positives and negatives while maintaining adaptability to unfamiliar signs.
Data Source
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AI summary
Aspects of the disclosure relate to determining a sign type of an unfamiliar sign 1053. The system may include one or more processors 120. The one or more processors may be configured to receive an image and identify image data corresponding to a traffic sign in the image. The image data corresponding to the traffic sign may be input in a sign type model 470. The processors may determine that the sign type model was unable to identify a type of the traffic sign and determine one or more attributes of the traffic sign. The one or more attributes of the traffic sign may be compared to known attributes of other traffic signs and based on this comparison, a sign type of the traffic sign may be determined. A vehicle 100 may be controlled in an autonomous driving mode based on the sign type of the traffic sign.