US-12623632-B2 - Theft event detection and classification

US12623632B2US 12623632 B2US12623632 B2US 12623632B2US-12623632-B2

Abstract

Disclosed are various embodiments related to theft event detection and classification of theft of tires and/or wheels on a vehicle, such as a semi-tractor, semi-trailer. A theft event detection and classification system can include at least one computing device including a vehicle computing device comprising an accelerometer. The system can also include tire sensors, corresponding to tires on the vehicle, and in data communication with the vehicle computing device. The system can execute a theft monitoring application to detect vibration signals by the accelerometer and generate an event data record comprising the vibration signal information, tire sensor records comprising retrieved sensor information, and a vehicle environment record. The theft event detection and classification system can execute a theft detection application to analyze the event data record to classify the event and a notification of the possible theft event.

Inventors

Brian Richard Morris
Micah John Thorn
Mateusz Andrzej Guzek

Assignees

THE GOODYEAR TIRE & RUBBER COMPANY

Dates

Publication Date: 20260512
Application Date: 20240418

Claims (16)

1 . A system, comprising: at least one computing device comprising a processor and memory; machine-readable instructions stored in the memory that, when executed by the processor, cause the at least one computing device to at least: receive event data from a vehicle computing device, the vehicle computing device attached to a vehicle having at least one tire sensor corresponding to at least one tire on the vehicle, the at least one tire sensor in data communication with the vehicle computing device, the event data comprising vibration signal information and sensor information collected during an event; analyze the vibration signal information of the event data to identify known vibration signatures; predict the event as possible theft event or non-theft event based in part on the identified vibration signatures; examine the event data for at least one theft classification factor, wherein the at least one theft classification factor is based at least in part on the identified vibrational signatures and at least one of a vehicle speed, a vehicle location, a time of day, a duration of the event, a tire pressure, and a status of individual ones of the at least one tire sensor; classify the theft event in response to the examination of the event data; and generate a notification of the possible theft event.
2 . The system of claim 1 , wherein the instructions, when executed by the processor, cause the at least one computing device to further classify a type of theft based on the identified vibration signatures of the event data corresponding to the known vibration signatures associated with at least one of: a tire theft, a full wheel theft, a tire replacement, or other theft.
3 . The system of claim 1 , wherein the instructions, when executed by the processor, cause the at least one computing device to further classify the theft event to include at least one: theft of a steer wheel, theft of a drive wheel, theft of a trailer wheel, or theft of cargo.
4 . The system of claim 1 , wherein the instructions, when executed by the processor, further cause the at least one computing device to at least: compare the event data to supplemental data stored regarding the vehicle; and identify a pattern indicating the possible theft event.
5 . The system of claim 1 , wherein, in response to the event predicted as a non-theft event, the instructions, when executed by the processor, further cause the at least one computing device to at least analyze additional data to increase confidence that the event is the non-theft event.
6 . A method, comprising: receiving, by at least one computing device, event data from a vehicle computing device, the vehicle computing device attached to a vehicle having at least one tire sensor corresponding to at least one tire on the vehicle, the event data comprising vibration signal information and sensor information collected during an event; analyzing, by the at least one computing device, the vibration signal information of the event data to identify known vibration signatures; predicting, by the at least one computing device, the event as possible theft event or non-theft event based in part on the identified vibration signatures; examining, by the at least one computing device, the event data for at least one theft classification factor, wherein the at least one theft classification factor is based at least in part on the identified vibrational signatures and at least one of a vehicle speed, a vehicle location, a time of day, a duration of the event, a tire pressure, and a status of individual ones of the at least one tire sensor; classifying, by the at least one computing device, the theft event; and generating, by the at least one computing device, a notification of the possible theft event.
7 . The method of claim 6 , further comprising classifying, by the at least one computing device, a type of theft based on the identified vibration signatures of the event data corresponding to the known vibration signatures associated with at least one of: a tire theft, a full wheel theft, a tire replacement, or other theft.
8 . The method of claim 6 , further comprising classifying, by the at least one computing device, the theft event to include at least one: theft of a steer wheel, theft of a drive wheel, theft of a trailer wheel, and theft of cargo.
9 . The method of claim 6 , further comprising: comparing the event data to supplemental data stored regarding the vehicle; and identifying a pattern indicating the possible theft event.
10 . The method of claim 6 , wherein, in response to the event predicted as a non-theft event, and further comprising analyzing additional data to increase confidence that the event is the non-theft event.
11 . A system, comprising: a vehicle computing device comprising an accelerometer, a processor, and memory, the vehicle computing device attached to a vehicle; at least one tire sensor corresponding to at least one tire on the vehicle, the at least one tire sensor in data communication with the vehicle computing device; machine-readable instructions stored in the memory that, when executed by the processor, cause the vehicle computing device to at least: detect vibration signals comprising at least one vibration measurement by the accelerometer; capture vibration signal information associated with the vibration signals detected; communicate with the at least one tire sensor to retrieve sensor information; generate an event data record comprising the vibration signal information, tire sensor records comprising the retrieved sensor information, and a vehicle environment record; transmit the event data records to a theft detection system; examine the event data for at least one theft classification factor, wherein the at least one theft classification factor is based at least in part on the identified vibrational signatures and at least one of a vehicle speed, a vehicle location, a time of day, a duration of the event, a tire pressure, and a status of individual ones of the at least one tire sensor; and classify the theft event in response to the examination of the event data.
12 . The system of claim 11 , wherein, the instructions, when executed by the processor, cause the at least one computing device to further evaluate the at least one vibration measurement based on at least one threshold.
13 . The system of claim 12 , wherein, the instructions, when executed by the processor, cause the at least one computing device to further, in response to the at least one vibration measurement being greater than the at least one threshold, enable broadcast of data to a remote computing environment.
14 . The system of claim 11 , wherein, the instructions, when executed by the processor, cause the at least one computing device to communicate with at least a location device on the vehicle; and generate the vehicle environment record comprising a location and a timestamp of the event.
15 . The system of claim 14 , wherein, the instructions, when executed by the processor, cause the at least one computing device to communicate with at least one additional sensor to determine a status of the vehicle, wherein the vehicle environment record further comprises the status of the vehicle.
16 . The system of claim 11 , wherein the tire sensor records further comprise previously retrieved tire sensor information.

Description

BACKGROUND Semi-trucks are large transportation vehicles that transport a load from a first location to a second location. A semi-truck can represent a semi-tractor for pulling a semi-trailer, in which the semi-trailer contains the load. In some scenarios, the owner of the semi-tractor may not own the semi-trailer. The semi-tractor can be used to haul different semi-trailers to various destinations. In some instances, a semi-trailer can be situated at a location waiting for a semi-tractor to arrive and deliver the semi-trailer to another location. After the semi-trailer has been used to deliver the load, the semi-trailer may be stored in a facility until it is needed for another delivery. BRIEF DESCRIPTION OF THE DRAWINGS Many aspects of the present disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, with emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views. FIG. 1 is a drawing of a network environment according to various embodiments of the present disclosure. FIG. 2 is a flowchart illustrating one example of functionality implemented as portions of a theft monitoring application executed in a computing environment in the networked environment of FIG. 1 according to various embodiments of the present disclosure. FIG. 3 is a flowchart illustrating one example of functionality implemented as portions of the theft detection system executed in a computing environment in the networked environment of FIG. 1 according to various embodiments of the present disclosure. DEFINITIONS “TPMS” means a tire pressure monitoring system, which is an electronic system that measures the internal pressure of a tire and is capable of communicating the pressure to a processor that is mounted on the vehicle and/or is in electronic communication with electronic systems of the vehicle and/or the trailer computing device. DETAILED DESCRIPTION The present disclosure relates to theft event detection and classification related to a vehicle in accordance to various embodiments. As a non-limiting example, a vehicle can be a semi-truck, which can refer a tractor vehicle (also referred to as “semi-tractor vehicle,” “semi-tractor,” “tractor” herein) that pulls a semi-trailer (also referred to as “trailer vehicle” or “trailer” herein). Although a trailer generally does not have front axle or engine, it is also referred to as a vehicle herein. In some instances, the semi-tractor and/or semi-trailer can be vulnerable to tire and/or full wheel theft, where a full wheel refers to a tire mounted on a wheel or a wheel hub that attaches to the axle of the vehicle. Thieves may attempt to steal or replace tires, or full wheels, from the tractor vehicle and/or the semi-trailer. For example, a semi-trailer can be detached from a semi-tractor and stored separately from the tractor vehicle in a remote location, thus can be vulnerable to tire theft activity. As another example, drivers of the tractor vehicles may participate in theft activity occurring with trailers. As a non-limiting example, the driver may provide location information of trailer and information on vulnerable trailers to an individual (e.g., a thief) in order to assist the individual in planning a theft of tires or full wheels. In another non-limiting example, a driver may also be complicit by pulling over to meet a thief for quick tire or wheel theft to occur, allowing the thief to remove the tires and/or wheels and replace with lesser quality tires and/or wheels, and the driver continues the route. As such, tires and full wheels of the tractor vehicles and semi-trailers can be vulnerable to illegal criminal activity because of the unique circumstances related to the use of these vehicles. Accordingly, various embodiments are directed to a need for detecting and classifying tire and/or full wheel theft activity that may arise from semi-tractor vehicles and semi-trailer vehicles. According to various examples of the present disclosure, a vehicle (e.g., semi-tractor and/or semi-trailer) can be monitored to detect vibrations that may relate to unexpected events, such as theft of one or more tires and/or full wheel assemblies (e.g., tire and wheel). The theft event detection and classification system disclosed herein can be implemented to utilize data collected from sensors on the vehicle and/or tires to classify the type of event. Vehicles, such as semi-trucks, can be equipped with tire sensors mounted on the inside of the tire of each wheel on the vehicle to obtain information regarding physical parameters of the tire, such as, for example, tire pressure, temperature, and the like. Information regarding tire identification can also be provided by RFID tags on the individual tires. In a non-limiting example, a sensor can be implemented to actively scanning for