Fleet Flood Detection Using Distributed Sensor Data
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Solution Overview
Problem
Flooding in geographic regions with substantial seasonal precipitation poses a significant risk to vehicles, causing damage and trapping vehicles in flooded areas, with existing technologies lacking effective methods to detect flooding and alert vehicles in real-time.
Innovation Solution
A cloud-based system and method that utilizes cameras, water level sensors, and vehicle position sensors mounted on host vehicles to detect flooding and send real-time alerts to affected vehicles, leveraging image-based and sensor-based data to identify flooded areas and provide alternative routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cloud-based system with multiple sensors and cameras is deployed to detect flooding in real-time, then the reliability of flood detection is improved, but the device complexity increases
Solution Approach 1:
The system divides the fleet into multiple vehicles, each equipped with sensors and cameras that independently detect flooding. Each vehicle operates as an autonomous detection unit, and the cloud system aggregates data from these segmented sources to improve overall detection reliability while distributing system complexity across multiple simple units rather than one complex centralized system.
Solution Approach 2:
The flood detection system is integrated into existing vehicle infrastructure, with sensors and cameras serving multiple purposes (e.g., water level detection, road condition monitoring, navigation). This multi-functionality allows the system to achieve reliable flood detection without adding dedicated complex equipment solely for flooding surveillance.
2Reliability
If real-time flood detection and alerting is implemented across a fleet, then vehicle safety is improved, but the use of energy increases due to continuous monitoring and communication
Solution Approach 1:
Instead of continuous high-power transmission, the system uses periodic updates where vehicles transmit flood detection data and location information at intervals or when significant changes occur. The cloud system processes these periodic updates and sends alerts only when necessary, reducing energy consumption while maintaining real-time safety monitoring capability.
Solution Approach 2:
Vehicles autonomously detect flooding conditions using their onboard sensors and cameras, process the data locally to determine flood status, and self-initiate communication with the cloud system only when flooding is detected. This self-service approach eliminates the need for continuous polling or transmission, significantly reducing energy consumption while maintaining safety.
3Measurement precision
If comprehensive flood detection data is collected and analyzed, then the measurement precision of flood conditions is improved, but the loss of time for data processing increases
Solution Approach 1:
Vehicles continuously collect and pre-process flood detection data (water level readings, camera images) in real-time before cloud analysis is needed. The onboard systems perform initial filtering and validation of sensor data, preparing it for rapid cloud processing. This preliminary action ensures high measurement precision is achieved through comprehensive data collection without incurring processing delays when alerts are needed.
Solution Approach 2:
The system replaces complex centralized data processing with a distributed architecture where each vehicle independently processes its sensor data using onboard computational units. This substitution of centralized mechanical processing with distributed electronic processing reduces data transmission and processing time while maintaining high detection precision through localized real-time analysis.
Data Source
AI summary
A flood detection and alert system and method for use with a fleet of vehicles. The system and method gather information pertaining to flooding from a fleet of vehicles spread across a geographic area and, with this information, are better able to identify flooded areas and alert vehicles so that such areas can be avoided. By using a cloud-based system to gather and analyze real-time flooding information from a distributed fleet of vehicles, the system and method are able to leverage the benefits of large data for more accurate recognition of flooded areas and more effective remedial measures, such as alerts, notifications and/or alternative routes.