Moving-Vehicle Cloud Cover Estimation with Low-Cost Infrared Sensors
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Solution Overview
Problem
Current vehicle systems for estimating cloud cover are hindered by high costs and computational burdens, and they often require expensive high-dynamic range camera equipment, limiting platform flexibility and upgradability.
Innovation Solution
A system utilizing onboard and remotely-located computational subsystems with sensors and control modules to estimate cloud cover, employing algorithms to process data from IR thermometers, GPS, and air pressure sensors, and generate cloud maps using low-cost hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive high-dynamic range camera equipment is used to estimate cloud cover, then measurement precision is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent replaces expensive high-dynamic range camera equipment with low-cost infrared thermometer sensors that can be easily manufactured and deployed. The infrared sensors measure sky radiance temperature to estimate cloud cover, eliminating the need for complex camera systems while maintaining measurement capability through alternative physical principles.
Solution Approach 2:
The patent substitutes optical/mechanical camera systems with thermal sensing based on infrared radiation measurement. Instead of using cameras to capture visual information about clouds, the system uses infrared thermometers to measure thermal radiation from the sky, converting an optical problem into a thermal measurement problem that is solved with simpler, cheaper hardware.
2Measurement precision
If high-dynamic range camera equipment is used for cloud cover estimation, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The patent replaces expensive high-dynamic range camera equipment with low-cost infrared thermometer sensors that can be easily manufactured and deployed. The infrared sensors measure sky radiance temperature to estimate cloud cover, eliminating the need for complex camera systems while maintaining measurement capability through alternative physical principles.
3Measurement precision
If high-dynamic range camera equipment is used to estimate cloud cover, then measurement precision is improved, but computational burden increases
Solution Approach 1:
The patent substitutes optical/mechanical camera systems with thermal sensing based on infrared radiation measurement. Instead of using cameras to capture visual information about clouds, the system uses infrared thermometers to measure thermal radiation from the sky, converting an optical problem into a thermal measurement problem that is solved with simpler, cheaper hardware.
Solution Approach 2:
The patent changes the measurement parameter from visual intensity (requiring high-dynamic range imaging and complex processing) to thermal radiation temperature (measured directly by infrared sensors). This parameter transformation simplifies the computational requirements while maintaining the ability to distinguish between clear sky and cloud conditions based on temperature differences.
4Measurement precision
If current vehicle systems use expensive hardware for cloud cover estimation, then measurement precision is improved, but adaptability decreases
Solution Approach 1:
The patent replaces expensive high-dynamic range camera equipment with low-cost infrared thermometer sensors that can be easily manufactured and deployed. The infrared sensors measure sky radiance temperature to estimate cloud cover, eliminating the need for complex camera systems while maintaining measurement capability through alternative physical principles.
Solution Approach 2:
The patent makes the cloud cover estimation system adaptable to different vehicle platforms by using standardized infrared sensor components that can be integrated into various vehicle types. The system architecture separates the sensing function from the vehicle platform, allowing the same sensor and algorithm combination to work across different vehicle models and manufacturers, thereby improving versatility and platform flexibility.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate cloud cover estimation with reduced costs and improved flexibility, enhancing vehicle safety and performance by providing real-time cloud cover information without increasing manufacturing or operational expenses.
Implementation Method 1
thermal data from an infrared (IR) thermometer
Implementation Method 2
ambient air pressure, ambient air temperature, and humidity from an air pressure sensor
Data Source
AI summary
A system for estimating cloud cover from moving vehicles includes: vehicle mounted sensors communicating with one or more control modules. The control modules have processors, memory, and input/output (I/O) ports. The I/O ports of the control modules of the vehicles communicate with the sensors. The control modules execute program code stored in the memory including first and second algorithm portions. The first algorithm portion, collects sensor data from the sensors, estimates atmospheric parameters from the sensor data; and computes measurement coordinates from the sensor data and atmospheric parameters. The second algorithm portion generates and selectively updates a cloud map based on from data received from the first algorithm portion, and from high-definition map (HD Map) data.