Driver passenger detection using ultrasonic sensing
Aspects of the disclosure relate to using ultrasonic or other types of signals to detect a driver in a vehicle. A computing platform may receive ultrasonic sensing data associated with mobile devices in the vehicle from a signal transmitter. Unique identifiers of the mobile devices may be determined. 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. The computing platform may use a machine learning classifier to determine that a particular occupant is a driver in the vehicle based on the relative distance.
1 . A computer-implemented method comprising:
receiving, by a computing device, ultrasonic sensing data that comprises respective frequency signatures associated with each of a plurality of mobile devices positioned within a vehicle during a particular period;
determining, by the computing device and based on the respective frequency signatures, relative distances of the plurality of mobile devices from an ultrasonic transmitter positioned within the vehicle; and
after determining, based on the relative distances, that a particular mobile device is closest to the ultrasonic transmitter, determining, by the computing device, the particular mobile device to be positioned proximate to a driving position of the vehicle.
2 . The method of claim 1 , further comprising:
receiving, by the computing device, sensor data from a sensor array associated with the vehicle; and
wherein determining the particular mobile device to be positioned proximate to the driving position of the vehicle further comprises determining, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular mobile device is positioned proximate to the driving position 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 comprises 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 mobile device to be positioned proximate to the driving position of the vehicle comprises:
prior to using the machine learning classifier, training, using training data comprising predefined labels associated a set of mobile devices in the vehicle, the machine learning classifier to output predicted labels for a plurality of mobile devices associated with 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 position label of the particular mobile device indicating whether the particular mobile device is positioned proximate to the driving position 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 respective frequency signatures correspond to a plurality of different frequencies, wherein each frequency corresponds to one of the plurality of the mobile devices in the vehicle.
8 . The method of claim 7 , wherein each frequency corresponds to a communication channel between the ultrasonic transmitter and the corresponding mobile device.
9 . A computing system comprising:
one or more processors; and
one or more instruction storage units that comprises instruction code executable by the one or more processors to cause the computing system to perform operations comprising:
receiving ultrasonic sensing data that comprises respective frequency signatures associated with each of a plurality of mobile devices positioned within a vehicle during a particular period;
determining, based on the respective frequency signatures, relative distances of the plurality of mobile devices from an ultrasonic transmitter positioned within the vehicle; and
after determining, based on the relative distances, that a particular mobile device is closest to the ultrasonic transmitter, determining the particular mobile device to be positioned proximate to a driving position of the vehicle.
10 . The computing system of claim 9 , wherein the instruction code is executable by the one or more processors to cause the computing system to perform further operations comprising:
receiving sensor data from a sensor array associated with the vehicle; and
wherein determining the particular mobile device to be positioned proximate to the driving position of the vehicle further comprises determining, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular mobile device is positioned proximate to the driving position of the vehicle.
11 . The computing system of claim 10 , wherein the sensor array comprises an infrared sensor, a sound sensor, a pressure sensor and a motion sensor.
12 . The computing system of claim 10 , wherein the machine learning classifier comprises a supervised machine learning classifier and an unsupervised machine learning classifier.
13 . The computing system of claim 10 , wherein determining, using the machine learning classifier, that the particular mobile device to be positioned proximate to the driving position of the vehicle comprises:
prior to using the machine learning classifier, training, using training data comprising predefined labels associated a set of mobile devices in the vehicle, the machine learning classifier to output predicted labels for a plurality of mobile devices associated with 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 position label of the particular mobile device indicating whether the particular mobile device is positioned proximate to the driving position of the vehicle.
14 . The computing system of claim 13 , 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.
15 . The computing system of claim 9 , wherein the respective frequency signatures correspond to a plurality of different frequencies, wherein each frequency corresponds to one of the plurality of the mobile devices in the vehicle.
16 . The computing system of claim 15 , wherein each frequency corresponds to a communication channel between the ultrasonic transmitter and the corresponding mobile device.
17 . A non-transitory computer-readable medium comprising instruction code executable by one or more processors of a computing system to cause the computing system to perform operations comprising:
receiving ultrasonic sensing data that comprises respective frequency signatures associated with each of a plurality of mobile devices positioned within a vehicle during a particular period;
determining, based on the respective frequency signatures, relative distances of the plurality of mobile devices from an ultrasonic transmitter positioned within the vehicle; and
after determining, based on the relative distances, that a particular mobile device is closest to the ultrasonic transmitter, determining the particular mobile device to be positioned proximate to a driving position of the vehicle.
18 . The non-transitory computer-readable medium of claim 17 , wherein the instruction code is executable by the one or more processors to cause the computing system to perform further operations comprising:
receiving sensor data from a sensor array associated with the vehicle; and
wherein determining the particular mobile device to be positioned proximate to the driving position of the vehicle further comprises determining, using a machine learning classifier and based on the ultrasonic sensing data and the sensor data, that the particular mobile device is positioned proximate to the driving position 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 machine learning classifier comprises a supervised machine learning classifier and an unsupervised machine learning classifier.