Map UI Superimposing Travel Route Patterns and Predictions
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Solution Overview
Problem
Current connected vehicle solutions face challenges in visualizing travel pattern analysis and prediction results, making it difficult for users to understand causal relations between real data and prediction outcomes, and complicating the debugging of navigation systems, especially with long trip distances and numerous check points.
Innovation Solution
A method and system for providing a user interface that superimposes travel route patterns and new travel route predictions on a map, using machine learning to analyze travel history data and display patterns in different manners based on prediction probability, allowing users to visualize and understand causal relations through feature points and playback along a time axis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If travel history data and prediction results are displayed using conventional methods, then the information can be shown to users, but it becomes difficult for users to understand causal relations between real data and prediction outcomes
Solution Approach 1:
The patent segments the travel route into multiple check points and divides the display into different visual layers: actual travel routes, predicted routes, and feature points. This segmentation allows users to understand causal relations by visually connecting specific feature points at check points with prediction outcomes, rather than presenting overwhelming continuous data.
Solution Approach 2:
The patent introduces feature points as intermediary visual elements that mediate between raw travel history data and prediction results. These feature points mark significant locations along the route and serve as visual anchors that help users understand the causal relationship between historical travel patterns and predicted outcomes.
2Ease of repair
If conventional display methods are used for navigation debugging, then basic information can be shown, but debugging becomes complicated especially with long trip distances and numerous check points
Solution Approach 1:
The patent segments the complex navigation system into observable components: actual routes, predicted routes, and feature points at check points. This segmentation enables developers to debug by visually inspecting specific segments rather than analyzing the entire complex system at once, making debugging easier despite long trip distances and numerous check points.
Solution Approach 2:
The patent uses different visual representations (color changes and display patterns) to differentiate between actual travel routes and predicted routes. This visual differentiation simplifies debugging by allowing developers to quickly identify and compare different data types without confusion, reducing the perceived system complexity.
3Quantity of substance
If multiple travel route patterns and predictions are displayed simultaneously, then comprehensive information is provided, but user understanding becomes difficult
Solution Approach 1:
The patent segments multiple travel route patterns into distinct visual categories and displays them in different manners. By dividing the information into manageable visual segments (actual routes, predicted routes, feature points), the system maintains comprehensive information quantity while improving user understanding through organized presentation.
Solution Approach 2:
The patent applies different display qualities to different parts of the information: feature points are highlighted at specific check points, actual routes are shown with one visual style, and predicted routes with another. This local differentiation allows users to understand the information hierarchy and causal relationships more easily while retaining comprehensive information.
Data Source
AI summary
Embodiments are directed to a user interface for superimposingly displaying, on a map, a plurality of travel route patterns reflecting tendencies of movements of a moving object and a new travel route predicted using the plurality of travel route patterns. The plurality of travel route patterns being extracted in advance by analyzing traveling history data of the moving object and being displayed in different manners, according to a prediction probability between each of the plurality of travel route patterns and the new travel route.