Closed Road Section Identification Using Vehicle Density Heat Maps
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Solution Overview
Problem
Existing systems for identifying closed road sections based on driving trajectory analysis, such as statistical and supervised learning approaches, are inefficient in updating maps timely and accurately when roads are closed due to reconditioning, rebuilds, or extreme weather conditions.
Innovation Solution
A system that generates heat maps representing vehicle density over time periods, determines difference maps, identifies candidate regions and links, and calculates confidence levels to pinpoint closed road sections using filtering operations, level set evolution, and R-Tree algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical approach or supervised learning approach is used to analyze driving trajectory information, then closed road sections can be identified, but the identification process is inefficient and cannot update maps timely
Solution Approach 1:
The patent replaces traditional statistical and supervised learning methods with a heat map-based visual analysis system. The system generates heat maps from driving trajectory data, allowing operators to visually identify closed road sections through color-coded density representations, thereby substituting complex computational algorithms with more efficient visual processing methods.
Solution Approach 2:
The patent introduces heat maps as an intermediary between raw driving trajectory data and closed road section identification. The heat maps serve as a visual mediator that transforms complex trajectory information into intuitive density patterns, enabling faster and more accurate identification of closed roads without requiring direct statistical analysis or machine learning processing.
2Reliability
If statistical approach or supervised learning approach is used to analyze driving trajectory information, then closed road sections can be identified, but the maps cannot be updated accurately and timely
Solution Approach 1:
The patent performs preliminary generation of heat maps from driving trajectory data before actual closed road identification is needed. By pre-processing the trajectory information into visual heat map formats, the system prepares the data in an easily analyzable state, enabling rapid and accurate identification of closed sections when updates are required, thus reducing both time and improving accuracy.
Solution Approach 2:
The patent substitutes traditional algorithm-based identification with visual heat map analysis, replacing complex computational processes with more efficient visual pattern recognition. This substitution enables both faster processing (reducing time loss) and more accurate identification (improving map update reliability).
Data Source
AI summary
The present disclosure relates to systems and methods for identifying a closed road section. The systems may obtain a first heat map representing a first density of tracked vehicles in a target area over a first time period and a second heat map representing a second density of tracked vehicles in the target area over a second time period; determine a difference map between the first heat map and the second heat map; determine one or more candidate regions based on the difference map; identify one or more candidate links associated with the one or more candidate regions in a road network map; determine one or more confidence levels associated with the one or more candidate links based on the one or more candidate regions; and identify one or more closed road sections based on the one or more confidence levels.