Mobile Device Black Ice Detection Using Local Classifier
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Solution Overview
Problem
Existing black ice detection systems that rely on internet connections or centralized servers become inaccessible during weather conditions associated with black ice, posing a risk to drivers who may mistakenly travel over black ice patches due to incorrect speed expectations.
Innovation Solution
A method utilizing a mobile device in a vehicle to receive image stream data, accelerometer data, temperature, and humidity data, and on-board dash vehicle data, with a classifier embedded to evaluate these inputs and determine the presence of black ice, even without internet connectivity, using computer vision and machine learning algorithms, and enabling communication between vehicles through direct connections or mesh networks for alert dissemination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data centers and internet connections are used for black ice detection, then measurement precision and data accuracy are improved, but reliability deteriorates during weather conditions that disrupt internet connectivity
Solution Approach 1:
The mobile device is designed to perform multiple functions: it serves as both a centralized data center capable of running complex classification algorithms locally and as a portable detection unit that can operate independently without internet connectivity. This multi-functionality allows the system to maintain high detection accuracy while ensuring reliability during adverse weather conditions by eliminating the single point of failure (internet dependency).
Solution Approach 2:
The classification algorithm and data processing capabilities are extracted from the centralized server infrastructure and embedded directly into the mobile device. This extraction eliminates the dependency on continuous internet connectivity and centralized data centers, allowing the system to maintain both high measurement precision through local processing and reliability during internet disruptions.
2Measurement precision
If dedicated sensors and centralized server infrastructure are deployed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The mobile device serves as a universal platform that combines multiple detection capabilities (image processing, sensor data collection, classification algorithms) into a single device. This eliminates the need for separate dedicated sensors and centralized server infrastructure, reducing overall system complexity while maintaining high detection precision through the integrated classification algorithm.
Solution Approach 2:
The mobile device performs self-service by running the classification algorithm locally without requiring external centralized server infrastructure. The device independently processes image stream data and sensor data, making the system simpler by eliminating complex infrastructure requirements while maintaining high measurement precision through local intelligent processing.
3Reliability
If internet connection dependency is reduced for local detection, then reliability is improved, but loss of information increases due to inability to access centralized data centers
Solution Approach 1:
The classification algorithm and essential data processing capabilities are extracted from centralized data centers and embedded into the mobile device. This extraction ensures that critical information processing functions remain available locally during internet disruptions, maintaining reliability while preventing information loss by keeping essential algorithms and data accessible without external connectivity.
Solution Approach 2:
The classification algorithm is pre-loaded and embedded in the mobile device before internet connectivity is needed. This preliminary action ensures that the device can immediately process data and provide black ice detection without requiring real-time internet access, maintaining both reliability during disruptions and preventing information loss by having essential processing capabilities already available locally.
Data Source
AI summary
A method of black ice detection includes receiving image stream data from a mobile device. The mobile device is in a vehicle. At least one of accelerometer data from at least one accelerometer, temperature and humidity data, and on board dash vehicle data is received. A classifier embedded in the mobile device is run to evaluate the received image stream data, and the at least one of the accelerometer data, the temperature and humidity data, and the on board dash vehicle data. It is determined whether black ice is present in the image stream data based on a result generated by the classifier.