Traffic Sign Attribute Matching for Unfamiliar Sign Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in responding to unfamiliar traffic signs, as existing systems rely on pre-mapped data that may be outdated or incomplete, leading to potential safety issues and travel delays when encountering new or temporary signs.
Innovation Solution
The method involves real-time detection of traffic signs using sensors and image processing techniques to determine attributes such as type, color, shape, and content, allowing the vehicle to respond appropriately without relying on pre-mapped information, and prioritizing requests for human assistance based on sign type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-mapped traffic sign data is used for autonomous vehicle decision-making, then the system operates with established information, but the data may be outdated or incomplete when encountering new or temporary signs
Solution Approach 1:
The system performs preliminary detection of traffic signs in the environment using sensors before the vehicle reaches them. By detecting signs ahead of time and comparing them against pre-mapped data, the system can identify unfamiliar signs and request human assistance in advance, rather than reacting when the sign is encountered.
Solution Approach 2:
The system continuously compares detected traffic signs with pre-mapped data and uses feedback loops to identify discrepancies. When unfamiliar signs are detected, the system requests human verification and uses this feedback to update its database, improving future recognition accuracy.
2Reliability
If the vehicle requests human assistance for every unfamiliar traffic sign, then accurate identification is achieved, but travel delays increase
Solution Approach 1:
The system requests human assistance only for a subset of unfamiliar signs that cannot be confidently identified through attribute comparison alone. By using partial action (requesting help only when necessary), the system maintains high accuracy while minimizing travel delays caused by human intervention.
Solution Approach 2:
The system changes the parameter of confidence threshold for requesting human assistance. By adjusting this parameter, the system can balance between requesting help more frequently (higher accuracy) or less frequently (less delay), optimizing performance based on conditions.
3Measurement precision
If real-time detection and attribute comparison is performed for all traffic signs, then unfamiliar signs are identified accurately, but computational complexity increases
Solution Approach 1:
The system segments the traffic sign recognition process into distinct stages: detection, attribute extraction, comparison with pre-mapped data, and decision-making. By dividing the complex task into smaller segments, the computational complexity at each stage is reduced while maintaining overall precision.
Solution Approach 2:
The system extracts only the essential attributes of detected traffic signs (such as shape, color, text) for comparison with pre-mapped data, rather than analyzing all possible characteristics. This extraction approach reduces computational complexity while maintaining sufficient precision for identification.
4Loss of information
If pre-mapped traffic sign data is relied upon, then the system has a database to compare against, but the data becomes outdated when signs are moved or replaced
Solution Approach 1:
The system performs self-updating by detecting traffic signs in the environment and comparing them with pre-mapped data. When discrepancies are found, the system requests human verification and automatically updates its database, enabling it to maintain current information without external intervention for every change.
Solution Approach 2:
The system transitions from a static pre-mapped database to a dynamic system that continuously updates its traffic sign information based on real-time detection and human feedback. This dynamic approach ensures the database remains current as signs are moved or replaced in the environment.
Data Source
AI summary
Aspects of the disclosure relate to determining a sign type of an unfamiliar sign. The system may include one or more processors. 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. 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. The vehicle may be controlled in an autonomous driving mode based on the sign type of the traffic sign.