Uncertainty-Based Map Visualizations for Vehicle Navigation
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Solution Overview
Problem
Existing vehicle navigation systems face challenges in accurately representing regions of interest with uncertainty, which can affect route optimization and vehicle operation, particularly in autonomous and semi-autonomous contexts.
Innovation Solution
A system and method that receive geographic coordinates of features of interest, define regions based on uncertainty levels, and generate visual representations on maps, using boundaries and networks of routes optimized by probability distributions and cellular automata patterns, to convey likelihood and significance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional map representations are used to show geographic locations, then the map is simple and easy to read, but the uncertainty associated with feature locations cannot be effectively communicated
Solution Approach 1:
The patent applies color changes to represent different levels of uncertainty in geographic features. Visual indicators such as colored boundaries or shading are used to encode uncertainty information, allowing the map to convey both location data and confidence levels without requiring separate data layers or complex interfaces.
Solution Approach 2:
The patent adds a visual dimension to represent uncertainty by overlaying graphical indicators (such as colored zones, confidence ellipses, or boundary layers) on top of the traditional map. This transforms the flat 2D map into a multi-layered visualization where the vertical dimension represents uncertainty magnitude, enabling simultaneous display of location and confidence information.
2Reliability
If regions are defined with extensive boundaries to account for uncertainty, then more area is covered and likelihood is captured, but the region size increases and precision is reduced
Solution Approach 1:
The patent applies different visual properties to different parts of the defined region based on local uncertainty characteristics. High-confidence areas are marked with distinct visual indicators (such as solid boundaries or intense coloring) while lower-confidence areas use different indicators (such as dashed boundaries or lighter shading), allowing users to quickly identify the most reliable location estimates without being overwhelmed by the entire expanded region.
Solution Approach 2:
The patent segments the defined region into multiple zones based on uncertainty levels. Each zone is visually distinguished to represent different confidence intervals or probability thresholds, enabling the system to communicate both the overall region extent and the specific high-confidence sub-areas separately, thus maintaining precision information within the broader uncertain region.
3Loss of information
If multiple visual attributes are added to represent uncertainty levels, then more information is conveyed, but the visual representation becomes more complex and harder to interpret
Solution Approach 1:
The patent uses a consistent color-coding scheme where specific colors or shades represent specific uncertainty levels. This standardized visual language allows users to quickly interpret uncertainty information without needing to learn multiple symbols or conventions. The color changes are applied systematically across all features, maintaining consistency and reducing cognitive load.
Solution Approach 2:
The patent uses simplified visual copies or proxies to represent complex uncertainty data. Instead of displaying raw probability values or statistical measures, the system creates visual representations (such as colored overlays, boundary lines, or icon variations) that copy the essential meaning of the uncertainty data in an easily interpretable format, maintaining the relationship between data value and visual representation.
Data Source
AI summary
A system for determining and presenting visual representations includes a receiving module configured to receive a set of coordinates representing one or more geographic locations of one or more features of interest, and an analysis module configured to define a region corresponding to the set of coordinates, the region having an extent based on a level of uncertainty associated with the one or more features of interest. The system also includes a visualization module configured to generate a visual representation of the defined region and present the visual representation on a map.