Traffic Sign Recognition via Hidden Markov Model
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Solution Overview
Problem
Existing traffic sign recognition methods face challenges in reliability, especially in poor visibility conditions, and require costly and time-consuming development of hard-coded acceptance criteria, leading to potential confusion between similar signs and incorrect recognition.
Innovation Solution
A method using a hidden Markov model to calculate the probability of traffic sign recognition based on previous states and transition probabilities, eliminating the need for hard-coded criteria and improving reliability by weighting provisional and first probability values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional matching methods are used for traffic sign recognition, then the system is simple to implement, but recognition reliability deteriorates in poor visibility conditions
Solution Approach 1:
The patent introduces map data as an intermediary element to mediate between camera data and traffic sign recognition. The fusion of camera-based visual recognition with map-based geographic information creates a more reliable recognition system that can distinguish actual traffic signs from visual artifacts in poor visibility conditions
Solution Approach 2:
The system implements feedback mechanisms where recognition results are continuously evaluated and refined. The evaluation unit assesses recognition outcomes and uses this feedback to improve subsequent recognitions, particularly in challenging visibility conditions where initial recognition may be uncertain
2Measurement precision
If hard-coded acceptance criteria are developed to improve recognition accuracy, then recognition precision improves, but development time and cost increase
Solution Approach 1:
The system performs self-training by automatically learning from training data consisting of images and corresponding traffic sign information. This self-service capability eliminates the need for manual hard-coding of acceptance criteria, as the evaluation unit develops its own recognition thresholds and parameters through automated learning processes
Solution Approach 2:
The patent dynamically adjusts recognition parameters and thresholds based on learned patterns from training data. The system modifies its evaluation criteria adaptively rather than relying on fixed hard-coded parameters, allowing precision improvement without proportional increases in development time
3Ease of operation
If camera data alone is used for traffic sign recognition, then the system is simple to operate, but reliability deteriorates when no camera data is available
Solution Approach 1:
The system achieves multi-functionality by integrating multiple data sources (camera data and map data) and multiple recognition approaches. It can operate using camera data when available, switch to map data when camera data is unavailable, or fuse both sources when both are available, making the system both versatile and reliable across different operating conditions
Data Source
AI summary
A method for recognizing traffic signs includes: receiving images of traffic signs from different locations at different times; and calculating a first probability value that indicates the probability with which an image received at a specific time maps a specific traffic sign from a set of traffic signs. The calculating is based on: at least one image of a traffic sign received before the specific time and characterizing an earlier state, and a previously known transition probability value that indicates the probability with which the specific traffic sign occurs following the earlier state.