Patent Application Titled “Driver Passenger Detection Using Ultrasonic Sensing” Published Online (USPTO 20220410906): Allstate Insurance Company

2023 JAN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor Isaac, Emad (Chicago, IL, US), filed on June 28, 2021, was made available online on December 29, 2022.

The assignee for this patent application is Allstate Insurance Company (Northbrook, Illinois, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Aspects of the disclosure relate generally to the processing and management of big data. In particular, aspects of the disclosure relate to detecting driver role using sensing data.

“When a group of people embark on a road trip, a vehicle may have multiple occupants and each occupant may carry one or more mobile devices. To track a driving pattern and provide insights into the trip, a server in a central office may attempt to communicate with a mobile device of a driver of the vehicle. However, conventional systems may not be able to readily identify which occupant is the driver of the vehicle, particularly when there are multiple mobile devices present simultaneously in a small confined space inside the vehicle.

“Aspects described herein may address these and other problems, and generally improve the quality, efficiency, and performance of driver detection and offer insights into the occupants of the vehicle.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “The following presents a simplified summary of various aspects described herein. This summary is not an extensive overview, and is not intended to identify required or critical elements or to delineate the scope of the claims. The following summary merely presents some concepts in a simplified form as an introductory prelude to the more detailed description provided below.

“Aspects described herein are directed towards driver passenger detection using ultrasonic sensing techniques. In accordance with one or more embodiments, a computing device may receive ultrasonic sensing data associated with various mobile devices from a signal transmitter mounted inside or outside of a vehicle. The signal transmitter may include a high-frequency ultrasonic transmitter. The mobile devices may be associated with different occupants in the vehicle. The computing device may determine a unique identifier associated with each mobile device. Based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each mobile device in the vehicle may be determined. Accordingly, the computing device may determine that a particular occupant is a driver of the vehicle by comparing the relative distances from the signal transmitter to each mobile device.

“In one or more instances, the computing device may receive sensor data from a sensor array associated with the vehicle. The sensor array may include an infrared sensor, a sound sensor, a pressure sensor and a motion sensor. Based on the ultrasonic sensing data and the sensor data, the computing device may use a machine learning classifier to determine that the particular occupant is the driver of the vehicle. The machine learning classifier may include a supervised machine learning classifier and an unsupervised machine learning classifier.

“In many aspects, the machine learning classifier may be trained using training data comprising predefined labels and the machine learning classifier may output predicted labels for occupants associated the vehicle. The sensor data and the ultrasonic sensing data may be subsequently provided as input to the trained machine learning classifier. The trained machine learning classifier may output a role label of the particular occupant indicating whether the particular occupant is a driver or a passenger of the vehicle.

“In many aspects, the computing device may determine a first set of predicted labels with corresponding confidence scores falling below a threshold value. The machine learning classifier may regenerate a second set of predicted labels, and the second set of predicted labels may have confidence scores above the threshold value.

“In many aspects, the computing device may receive the ultrasonic sensing data associated with a plurality of frequencies from the signal transmitter. Each frequency may correspond to a mobile device in the vehicle. Alternatively, each frequency may correspond to a communication channel between the signal transmitter and the corresponding mobile device. The computing device may store a frequency signature associated with a particular mobile device in a sensing database system. The frequency signature may indicate a mapping between an identifier of the particular mobile device and a particular frequency that the signal transmitter used to communicate with the particular mobile device.

“These features, along with many others, are discussed in greater detail below.”

The claims supplied by the inventors are:

“1. A computer-implemented method comprising: receiving, by a computing device and from a signal transmitter in a vehicle, ultrasonic sensing data associated with a plurality of mobile devices in the vehicle, wherein the plurality of mobile devices are associated with a plurality of occupants in the vehicle; determining, by the computing device, a unique identifier associated with each of the plurality of mobile devices; determining, based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each of the plurality of mobile devices in the vehicle; and determining that a particular occupant, from the plurality of occupants in the vehicle, is a driver in the vehicle by comparing the relative distance from the signal transmitter to each of the plurality of mobile devices.

“2. The method of claim 1, further comprising: receiving, by the computing device, sensor data from a sensor array associated with the vehicle; and determining, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular occupant is the driver of the vehicle.

“3. The method of claim 2, wherein the sensor array comprises an infrared sensor, a sound sensor, a pressure sensor and a motion sensor.

“4. The method of claim 2, wherein the machine learning classifier comprise a supervised machine learning classifier and an unsupervised machine learning classifier.

“5. The method of claim 2, wherein determining, using the machine learning classifier, that the particular occupant is the driver of the vehicle comprises: prior to using the machine learning classifier, training, using training data comprising predefined labels associated a set of occupants in the vehicle, the machine learning classifier to output predicted labels for occupants associated the vehicle; providing, as input to the trained machine learning classifier, the sensor data and the ultrasonic sensing data; and receiving, as output from the trained machine learning classifier and based on the sensor data and the ultrasonic sensing data, a role label of the particular occupant indicating whether the particular occupant is the driver of the vehicle.

“6. The method of claim 5, wherein training the machine learning classifier comprises: generating, using the machine learning classifier, a first set of predicted labels; determining that the first set of predicted labels have corresponding confidence scores falling below a threshold value; and regenerating, using the machine learning classifier, a second set of predicted labels, wherein the second set of predicted labels having confidence scores above the threshold value.

“7. The method of claim 1, wherein the signal transmitter comprises a high-frequency ultrasonic transmitter.

“8. The method of claim 1, further comprising: receiving, from the signal transmitter, the ultrasonic sensing data associated with a plurality of frequencies, wherein each frequency corresponds to one of the plurality of the mobile devices in the vehicle.

“9. The method of claim 8, wherein each frequency corresponds a communication channel between the signal transmitter and the corresponding mobile device.

“10. The method of claim 7, further comprising: storing, in a sensing database, a frequency signature associated with a particular mobile device, wherein the frequency signature indicates a mapping between an identifier of the particular mobile device and a particular frequency that the signal transmitter used to communicate with the particular mobile device.

“11. A computing device comprising: one or more processors; memory storing instructions that, when executed by the one or more processors, cause the computing device to: receive, from a signal transmitter in a vehicle, ultrasonic sensing data associated with a plurality of mobile devices in the vehicle, wherein the plurality of mobile devices are associated with a plurality of occupants in the vehicle; determine a unique identifier associated with each of the plurality of mobile devices; determine, based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each of the plurality of mobile devices in the vehicle; and determine that a particular occupant, from the plurality of occupants in the vehicle, is a driver in the vehicle by comparing the relative distance from the signal transmitter to each of the plurality of mobile devices.

“12. The computing device of claim 11, wherein the instructions, when executed by the one or more processors, cause the computing device to: receive sensor data from a sensor array associated with the vehicle; and determine, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular occupant is the driver of the vehicle.

“13. The computing device of claim 12, wherein the sensor array comprises an infrared sensor, a sound sensor, a pressure sensor and a motion sensor.

“14. The computing device of claim 12, wherein the machine learning classifier comprises a supervised machine learning classifier and an unsupervised machine learning classifier.

“15. The computing device of claim 12, wherein the instructions, when executed by the one or more processors, cause the computing device to: prior to using the machine learning classifier, train, using training data comprising predefined labels associated a set of occupants in the vehicle, the machine learning classifier to output predicted labels for occupants associated the vehicle; provide, as input to the trained machine learning classifier, the sensor data and the ultrasonic sensing data; and receive, as output from the trained machine learning classifier and based on the sensor data and the ultrasonic sensing data, a role label of the particular occupant indicating whether the particular occupant is the driver of the vehicle.

“16. The computing device of claim 12, wherein the instructions, when executed by the one or more processors, cause the computing device to: generate, using the machine learning classifier, a first set of predicted labels; determine that the first set of predicted labels have corresponding confidence scores falling below a threshold value; and regenerate, using the machine learning classifier, a second set of predicted labels, wherein the second set of predicted labels having confidence scores above the threshold value.

“17. A non-transitory computer-readable medium storing instructions that, when executed, configure a computing device to: receive, from a signal transmitter in a vehicle, ultrasonic sensing data associated with a plurality of mobile devices in the vehicle, wherein the plurality of mobile devices are associated with a plurality of occupants in the vehicle; determine a unique identifier associated with each of the plurality of mobile devices; determine, based on the ultrasonic sensing data and the unique identifier, a relative distance from the signal transmitter to each of the plurality of mobile devices in the vehicle; and determine that a particular occupant, from the plurality of occupants in the vehicle, is a driver in the vehicle by comparing the relative distance from the signal transmitter to each of the plurality of mobile devices.

“18. The non-transitory computer-readable medium of claim 17, wherein the instructions, when executed, configure the computing device to: receive sensor data from a sensor array associated with the vehicle; and determine, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular occupant is the driver of the vehicle.

“19. The non-transitory computer-readable medium of claim 18, wherein the sensor array comprises an infrared sensor, a sound sensor, a pressure sensor and a motion sensor.

“20. The non-transitory computer-readable medium of claim 18, wherein the instructions, when executed, configure the computing device to: prior to using the machine learning classifier, train, using training data comprising predefined labels associated a set of occupant in the vehicle, the machine learning classifier to output predicted labels for occupants associated the vehicle; provide, as input to the trained machine learning classifier, the sensor data and the ultrasonic sensing data; and receive, as output from the trained machine learning classifier and based on the sensor data and the ultrasonic sensing data, a role label of the particular occupant indicating whether the particular occupant is the driver of the vehicle.”

For more information, see this patent application: Isaac, Emad. Driver Passenger Detection Using Ultrasonic Sensing. U.S. Patent Application Number 20220410906, filed June 28, 2021 and posted December 29, 2022. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20220410906)&db=US-PGPUB&type=ids

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