Trailer Tire Theft Detection Using Pressure and Motion Sensing
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Solution Overview
Problem
Semi-tractor trailers are vulnerable to tire and tire wheel theft due to their unique circumstances, such as storage in remote locations and potential involvement of drivers in theft activities, necessitating a system to identify and prevent such theft.
Innovation Solution
A networked environment with a computing environment, trailer computing device, tire sensors, and machine learning models is employed to detect theft activity by analyzing sensor data from accelerometers, location devices, and transceivers, generating heat maps to identify theft patterns and alerting authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semi-trailers are stored in remote locations, then operational efficiency is improved, but vulnerability to tire and wheel theft increases
Solution Approach 1:
The system performs preliminary detection by continuously monitoring sensor data (accelerometers, transceivers, location devices) before theft occurs. The machine learning models analyze patterns in advance to identify potential theft risks, allowing preventive actions to be taken before actual theft happens at remote storage locations.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from trailers is constantly monitored and analyzed. When abnormal patterns are detected (such as unusual movements or attempts to remove tires/wheels), the system provides real-time feedback alerts to authorities, enabling responsive intervention to counter theft attempts at remote locations.
2Measurement precision
If multiple sensors and machine learning models are deployed, then theft detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the monitoring function into multiple specialized sensors (accelerometers for movement detection, transceivers for communication monitoring, location devices for tracking) distributed across different trailers. Each sensor type focuses on specific aspects of theft detection, improving overall accuracy while allowing modular deployment and management of complexity.
Solution Approach 2:
The machine learning models serve as intermediaries that process and integrate data from multiple complex sensors. These models translate raw sensor data into meaningful theft detection insights, simplifying the interface between complex sensor arrays and the final detection output, thereby managing system complexity while maintaining high detection accuracy.
Data Source
AI summary
Disclosed are various embodiments related to detecting tire theft activity. In one non-limiting example, a vehicle trailer system can include a computing device and a tire sensor. The computing device can be configured to be attached to a semi-trailer and the computing device can include an accelerometer. The tire sensor can be attached to a wheel of the trailer and in data communication with the computing device. The computing device can be configured to at least detect a change in height for a portion of the trailer based at least in part on a measurement from the accelerometer. A pressure loss event can be detected based at least in part on a plurality of pressure measurements meeting a rate loss threshold. A vibration measurement can be identified from the accelerometer. An indication of a theft event can be transmitted to a remote computing device.