Road Sign Visibility Prediction Using Weather and Attribute Data
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Solution Overview
Problem
Existing navigation systems fail to provide timely and accurate prediction of road sign visibility, especially under adverse weather conditions, leading to potential hazards for vehicles and operators who rely on map data or sensors after encountering obscured signs.
Innovation Solution
A system that uses road sign attribute data and weather forecast data, processed by a machine learning model, to predict the state of visibility for road signs, enabling early notification and adaptive sensor usage to improve safety and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If navigation systems rely on map data and sensors after encountering obscured road signs, then operators can determine road object information, but the operator loses time and reliability is reduced due to late detection
Solution Approach 1:
The system performs preliminary action by predicting road sign visibility before the vehicle actually encounters the sign. The machine learning model uses weather forecast data and road sign attribute data to forecast whether a sign will be visible or obscured in advance, allowing the operator to prepare for potential visibility issues before they occur, thereby eliminating the time loss associated with post-encounter detection.
2Reliability
If the system uses machine learning model to predict visibility, then reliability is improved, but device complexity increases due to data processing requirements
Solution Approach 1:
The patent introduces an intermediary approach by using a machine learning model as a mediator between raw input data (weather forecasts, road sign attributes) and the final visibility prediction. This intermediary model processes and synthesizes the complex relationships between multiple data sources, managing the computational complexity centrally rather than requiring complex processing at every stage, thus improving reliability while controlling overall system complexity.
3Measurement precision
If the system predicts visibility for multiple road signs, then navigation accuracy is improved, but resource allocation increases due to extensive image processing requirements
Solution Approach 1:
The patent applies mechanics substitution by replacing traditional mechanical image processing systems with a machine learning-based predictive system. Instead of continuously processing images of multiple road signs to determine visibility, the system uses trained machine learning models that take weather forecast data and road sign attribute data as input to directly predict visibility outcomes. This substitution dramatically reduces the computational resources and energy required while maintaining or improving navigation accuracy through more efficient data processing.
Data Source
AI summary
A method, apparatus and computer program product are provided for predicting a state of visibility for a road object. For example, at least one processor receives road sign attribute data indicating at least one attribute of a road sign. The processor further receives weather forecast data indicating a weather forecast of a location in which the road sign is disposed, and using the road sign attribute data and the weather forecast data, a state of visibility for the road sign is identified.