Visibility Scoring for Navigation via Visual Feature Analysis
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Solution Overview
Problem
Physical addresses alone are insufficient for navigation in unfamiliar areas, as they do not account for the visibility of signs or logos from various directions and times, making it difficult for ride-sharing and navigation systems to determine the best meeting location.
Innovation Solution
A machine learning-based visibility scoring system that analyzes visual data from images to generate a visibility score for places, considering factors like sign size, height, distance from the road, directionality, and time of day, to select the most visible location for directions or navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical addresses are used for navigation, then directions can be provided, but the addresses may not be visible or easy to see from the road or sidewalk
Solution Approach 1:
The patent replaces manual visual inspection of signs with automated image processing and machine learning algorithms. The system captures images of signs, extracts text and visual features, and automatically determines visibility scores, eliminating the need for manual assessment and improving both accuracy and efficiency.
Solution Approach 2:
The patent transforms the abstract concept of 'visibility' into measurable parameters including text size, height above ground, distance from road, lighting conditions, and occlusion levels. By quantifying visibility across multiple dimensions, the system can objectively compare different signs and select the most visible ones for navigation.
2Measurement precision
If multiple places are considered in a given location, then the best place can be selected, but the complexity of determining visibility factors becomes beyond human capability
Solution Approach 1:
The patent divides the complex visibility assessment task into distinct modular components: image capture, text extraction, feature detection (size, height, distance), lighting analysis, occlusion detection, and scoring. Each module handles a specific aspect of visibility assessment, making the overall system manageable and maintainable while achieving high precision.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw image data and visibility scores. These models automatically learn the complex relationships between various visual features and actual visibility, handling the computational complexity without requiring explicit programming of every visibility factor.
3Measurement precision
If visual data analysis is performed to determine visibility, then accurate visibility scores can be generated, but computational resources and time are required
Solution Approach 1:
The patent performs preliminary processing of visual data by pre-extracting key features (text, size, height, position) from images and storing them in structured formats. This preprocessing allows for faster retrieval and analysis when visibility scores are needed, reducing computation time during actual navigation queries.
Data Source
AI summary
Systems and methods are provided for receiving geographic coordinates for a location, determining a road segment associated with the location based on the geographic coordinates for the location, and determining a plurality of places associated with the road segment associated with the location. The systems and methods further provide for extracting visual data for each of the plurality of places, generating a plurality of feature values based on the visual data for each of the plurality of places, and analyzing the plurality of feature values to generate a visibility score for each of the plurality of places.