Vehicle Flooding Prediction and Autonomous Relocation Control
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Solution Overview
Problem
Existing vehicle flooding detection technologies are inadequate in quickly determining flooding situations, especially during rapid water level rises due to climate change, and fail to provide timely warnings or autonomous responses to prevent vehicle damage.
Innovation Solution
An apparatus and method that utilize a communication device, sensors, and an image acquisition device to collect and analyze weather and precipitation data, determine the vehicle's flooding state in real-time, predict future flooding situations, and autonomously navigate the vehicle to a safe location when flooding is imminent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sensors are used to monitor vehicle submersion, then the system can detect flooding conditions, but the response time is too slow to prevent damage during rapid water level rises
Solution Approach 1:
The system performs preliminary actions by predicting future flooding situations based on current weather data, precipitation data, and image analysis before actual flooding occurs. The controller predicts water level changes and issues warnings in advance, allowing timely vehicle relocation rather than waiting for sensors to detect actual submersion
Solution Approach 2:
The system segments the flooding detection process into multiple independent data sources: weather data acquisition, precipitation data collection, image data capture, and controller analysis. This segmentation allows parallel processing of multiple data streams, significantly improving response speed compared to single-sensor approaches
2Loss of time
If multiple sensors and data collection devices are added to improve flooding detection speed, then the response time improves, but the device complexity increases
Solution Approach 1:
The controller serves multiple functions: it processes weather data, analyzes precipitation data, evaluates image data for flooding conditions, predicts future flooding situations, generates warnings, and controls vehicle relocation. This multi-functionality reduces the need for separate dedicated components for each function, managing system complexity while maintaining fast response
Solution Approach 2:
The server acts as an intermediary between multiple data sources (weather stations, cameras, sensors) and the vehicle controller. It consolidates and processes data from various sources, sending integrated information to the controller, which simplifies the architecture compared to direct peer-to-peer connections between all components
3Loss of information
If the system provides real-time notifications to users about flooding risks, then the user awareness improves, but the loss of time for vehicle relocation may increase due to notification processing
Solution Approach 1:
The system sends preliminary warnings to users before flooding occurs, providing advance notice of predicted flooding situations. This allows users to prepare for and initiate vehicle relocation immediately upon receiving the warning, rather than waiting for actual flooding conditions to develop
Solution Approach 2:
The system implements feedback by continuously monitoring flooding conditions and updating users on the current state and predicted future conditions. This real-time feedback loop keeps users informed of changing situations, enabling them to make timely decisions about vehicle relocation based on current risk levels
Data Source
AI summary
An embodiment apparatus for controlling a vehicle includes a communication device configured to obtain weather data in an area where the vehicle is located, a sensor configured to obtain precipitation data, an image acquisition device configured to obtain image data around the vehicle, and a controller configured to determine a possibility of flooding of the vehicle based on the weather data obtained from the communication device and the precipitation data obtained from the sensor and, in response to a determination that it is possible for the vehicle to be flooded, determine a current state of the vehicle based on the image data and predict a future flooding situation based on the weather data, the precipitation data, or the image data.