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Application US20200143237
Published 2020-05-07
Detection Of Vehicle Riding Behavior And Corresponding Systems And Methods
In various embodiments, the present disclosure relates to systems, methods, and computer-readable media for the detection of vehicle (e.g., a scooter) riding behavior. In particular, a method is described, the method including: determining first sensor data received from one or more sensors associated with a device, wherein the first sensor data is associated with a vehicle and with a time domain; determining, by the at least one processor, based on the first sensor data, second sensor data associated with a frequency domain, wherein to determine the second sensor data comprises to perform a Fourier transform on the first sensor data to determine Fourier coefficients associated with the first sensor data; and determining, by the at least one processor, based on the Fourier coefficients, using a machine learning algorithm, a type of the vehicle.
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- 1. A device, comprising:
at least one memory that stores computer-executable instructions; and at least one processor of the one or more processors configured to access the at least one memory, wherein the at least one processor of the one or more processors is configured to execute the computer-executable instructions to:
determine first sensor data received from one or more sensors associated with the device, wherein the first sensor data is associated with a vehicle and with a time domain;
determine, based on the first sensor data, second sensor data associated with a frequency domain, wherein to determine the second sensor data comprises to perform a Fourier transform on the first sensor data to determine Fourier coefficients associated with the first sensor data; and
determine, based on the Fourier coefficients, using a machine learning algorithm, a type of the vehicle, wherein the type of the vehicle is associated with a scooter.
- 9. A method, comprising:
determining training data, the training data comprising first data associated with a first vehicle and second data associated with a second vehicle; determine first sensor data received from a first device, wherein the first sensor data is associated with the first vehicle and with a time domain; determine second sensor data received from a second device, wherein the second sensor data is associated with the second vehicle and with the time domain; determine, based on the first sensor data, third sensor data associated with a frequency domain, wherein to determine the third sensor data comprises to perform a Fourier transform on the first sensor data to determine first Fourier coefficients associated with the first sensor data; determine, based on the second sensor data, fourth sensor data associated with the frequency domain, wherein to determine the fourth sensor data comprises to perform a Fourier transform on the second sensor data to determine second Fourier coefficients associated with the second sensor data; determine, based on the first Fourier coefficients and the training data, using a machine learning algorithm, a type of the first vehicle, wherein the type of the first vehicle is associated with a scooter; and determine, based on the second Fourier coefficients and the training data, using the machine learning algorithm, a type of the second vehicle, wherein the type of the second vehicle is different than the type of the first vehicle.
- 17. A method, comprising:
determining, by at least one processor of a device, first sensor data received from one or more sensors associated with the device, wherein the first sensor data is associated with a vehicle and with a time domain; determining, by the at least one processor, based on the first sensor data, second sensor data associated with a frequency domain, wherein to determine the second sensor data comprises to perform a Fourier transform on the first sensor data to determine Fourier coefficients associated with the first sensor data; and determining, by the at least one processor, based on the Fourier coefficients, using a machine learning algorithm, a type of the vehicle, wherein the type of the vehicle is associated with a scooter.
