Roadway Anomaly Analytics Platform Using Sensor Filtering
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Solution Overview
Problem
Existing methods fail to efficiently identify and remediate roadway anomalies such as potholes in real-time, posing hazards to vehicles and requiring manual intervention for maintenance.
Innovation Solution
An analytics platform utilizing sensor systems mounted on vehicles to detect anomalies and apply filters to classify potholes as specific types, providing real-time data for remedial action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used for identifying and classifying roadway anomalies, then accuracy of anomaly classification is improved, but productivity and response time deteriorate
Solution Approach 1:
The system enables automated self-service classification of roadway anomalies through machine learning models that automatically analyze sensor data, generate anomaly classifications, and prioritize maintenance tasks without requiring manual human intervention for each anomaly detection and classification task
Solution Approach 2:
The patent replaces manual mechanical classification processes with automated electronic systems including sensor arrays, data processing circuits, and machine learning algorithms that automatically detect, classify, and prioritize roadway anomalies based on analyzed data patterns
2Reliability
If comprehensive sensor systems are deployed for real-time anomaly detection, then reliability of hazard identification is improved, but device complexity increases
Solution Approach 1:
The sensor system is designed with multi-functionality, where a single integrated platform performs multiple tasks including detecting various types of roadway anomalies (potholes, debris, ice), classifying them by severity, prioritizing maintenance tasks, and providing real-time alerts, thereby reducing the need for multiple separate specialized systems
Solution Approach 2:
The patent combines multiple detection functions into a single integrated sensor system that simultaneously captures data across different modalities (optical, thermal, mechanical sensors) and processes all anomaly detection, classification, and prioritization tasks through a unified machine learning model
3Loss of time
If real-time data processing is implemented for anomaly classification, then response time to hazards is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models offline and pre-processing sensor data streams to identify potential anomalies before they become critical hazards, allowing for proactive maintenance scheduling and reducing the need for intensive real-time processing of all data
Solution Approach 2:
The patent implements periodic action through scheduled maintenance assessments where the system continuously monitors but performs intensive classification and prioritization processing at periodic intervals rather than continuously, reducing peak energy consumption while maintaining effective hazard identification
Data Source
AI summary
An analytics platform may obtain anomaly information associated with a roadway anomaly. The anomaly information may be provided by a sensor system including a sensor array. The analytics platform may determine a set of filters associated with identifying the roadway anomaly as being of a particular type. The analytics platform may apply the set of filters to the anomaly information associated with the roadway anomaly. The analytics platform may identify, based on applying the set of filters to the anomaly information, the roadway anomaly as being of the particular type. The analytics platform may provide, based on identifying the roadway anomaly as being of the particular type, information associated with the roadway anomaly.