Road Condition Tagging via Fleet Image Classification
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Solution Overview
Problem
Current systems lack an efficient mechanism for fleet managers to obtain real-time information on road conditions along planned routes, relying on manual data retrieval from various sources and lacking detailed insights into road conditions such as snow coverage or road maintenance.
Innovation Solution
A system that utilizes a geospatial database to store road segments tagged with road conditions, employing machine learning models to classify images from fleet vehicles to determine road conditions, and implementing active measures such as alerts and route updates based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual data retrieval from multiple sources is used, then users can obtain road condition information, but the process is time-consuming and lacks real-time accuracy
Solution Approach 1:
The patent combines multiple data sources (weather data, road sensor data, fleet vehicle camera images) into a unified road condition assessment system. This integration allows comprehensive road condition information to be obtained simultaneously from all sources rather than manually retrieving each source separately, resolving the contradiction between information completeness and retrieval time.
Solution Approach 2:
The system implements feedback loops where road condition data from fleet vehicles is continuously collected, processed, and used to update road condition tags in real-time. This continuous feedback mechanism ensures that road condition information remains current and accurate without requiring manual refreshes or repeated data retrieval attempts.
2Measurement precision
If detailed road condition analysis (snow coverage, road maintenance status) is performed, then decision-making accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the road condition assessment into distinct components: weather condition analysis, road surface condition detection, maintenance status evaluation, and hazard identification. Each segment is handled by specialized processing modules that focus on specific aspects, allowing detailed analysis without overwhelming system complexity. This modular segmentation enables precise measurement of individual road conditions while maintaining manageable system architecture.
3Reliability
If real-time road condition monitoring is implemented across the entire fleet, then safety and efficiency improve, but data processing requirements and computational load increase
Solution Approach 1:
The system applies local quality by processing and analyzing road condition data specifically at locations where fleet vehicles are operating or planned to operate. Rather than continuously monitoring all possible road segments, the system focuses computational resources on geographically relevant areas, reducing overall computational energy consumption while maintaining safety and reliability for the specific fleet operations.
4Measurement precision
If multiple data sources are integrated for road condition assessment, then information accuracy improves, but data integration complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes and harmonizes data from multiple sources (weather services, road sensors, camera images). This intermediary layer acts as a mediator that translates different data formats and protocols into a unified structure, improving information accuracy while managing integration complexity through standardized interfaces and common data models.
Data Source
AI summary
The present application discloses a method, system, and computer system for determining a road condition classification for one or more road segments. The method includes (i) determining a set of one or more images for a particular geographic area, (ii) obtaining a set of one or more images for the particular geographic area, (iii) determining a set of road classifications for the set of road segments based at least in part on querying a machine learning model to classify the set of one or more images, and (iv) storing the set of road classifications in association with the set of road segments.