METHODS AND SYSTEMS FOR DETERMINING WHETHER AN OBJECT IS EMBEDDED IN A TIRE OF A VEHICLE

1 . A method comprising:

receiving audio data from a microphone positioned to capture sounds of a tire on a vehicle moving on a road;

obtaining a speed of the vehicle that overlaps with a same timeframe of when the sounds of the tire were captured by the microphone in the received audio data;

determining that a foreign object is embedded in the tire based on a frequency analysis of the received audio data relative to the speed of the vehicle; and

outputting an alert to a driver of the vehicle indicating the foreign object is embedded in the tire.

2 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

selecting a plurality of sound samples from the received audio data;

converting the plurality of sound samples into a series of coefficients representing frequency components of the received audio data;

determining an expected rate of rotation for the tire based on the speed of the vehicle;

in response to high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determining that the foreign object is embedded in the tire; and

in response to a lack of high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determining that no foreign object is embedded in the tire.

3 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

determining a rotation rate of the tire based on the speed and a circumference of the tire;

identifying an audio anomaly in the received audio data occurring at the determined rotation rate; and

determining that the foreign object is embedded in the tire in response to identification of the audio anomaly.

4 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

dividing the received audio data into data blocks whose size is calculated based on an expected length of time of a single rotation of the tire;

detecting an audio anomaly in the received audio data based on a degree of correlation between the data blocks; and

determining that the foreign object is embedded in the tire based on the audio anomaly.

5 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

performing a Pearson product-moment coefficient technique on the received audio data to detect a time correlated audio anomaly indicative of the foreign object being embedded in the tire.

6 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

employing a trained machine learning model on the received audio data to classify the received audio data as containing a frequency dependent anomaly indicative of the foreign object being embedded in the tire.

7 . The method of claim 1 , wherein determining that the foreign object is embedded in the tire includes:

determining that a nail or screw object is embedded in the tire based on the frequency analysis of the received audio data relative to the speed of the vehicle.

8 . A system comprising:

a microphone configured to capture sounds of a tire on a vehicle moving on a road;

an output interface configured to present an alert to a driver of the vehicle;

a memory configured to store computer instructions;

at least one processor configured to execute the computer instructions to:

receive, via the microphone, audio data for a time period;

obtain a speed of the vehicle during the time period;

determine that a foreign object is embedded in the tire based on a frequency analysis of the audio data relative to the speed of the vehicle; and

output, via the output interface, an alert to a driver of the vehicle indicating the foreign object is embedded in the tire.

9 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

generate a plurality of sound samples from the audio data;

convert the plurality of sound samples into a series of coefficients representing frequency components of the audio data;

determine an expected rate of rotation for the tire based on the speed of the vehicle;

in response to high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determine that the foreign object is embedded in the tire; and

in response to a lack of high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determine that no foreign object is embedded in the tire.

10 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

determine a rotation rate of the tire based on the speed and a circumference of the tire;

identify an audio anomaly in the audio data occurring at the determined rotation rate; and

determine that the foreign object is embedded in the tire in response to identification of the audio anomaly.

11 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

divide the audio data into data blocks whose size is calculated based on an expected length of time of a single rotation of the tire;

detect an audio anomaly in the audio data based on a degree of correlation between the data blocks; and

determine that the foreign object is embedded in the tire based on the audio anomaly.

12 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

perform a Pearson product-moment coefficient technique on the audio data to detect a time correlated audio anomaly indicative of the foreign object being embedded in the tire.

13 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

employ a trained machine learning model on the audio data to classify the audio data as containing a frequency dependent anomaly indicative of the foreign object being embedded in the tire.

14 . The system of claim 8 , wherein the processor is configured to determine that the foreign object is embedded in the tire by executing further computer instructions to:

determine that a nail or screw object is embedded in the tire based on the frequency analysis of the audio data relative to the speed of the vehicle.

15 . A computing device comprising:

a memory configured to store computer instructions; and

at least one processor configured to execute the computer instructions to:

obtain audio data from a microphone positioned to capture sounds of a tire on a vehicle moving on a road;

determine a speed of the vehicle that corresponds to when the microphone captured the sounds of the tire;

determine that a object is embedded in the tire based on a frequency analysis of the audio data relative to the speed of the vehicle; and

output, via an output interface, an alert to a driver of the vehicle indicating the object is embedded in the tire.

16 . The computing device of claim 15 , wherein the processor is configured to determine that the object is embedded in the tire by executing further computer instructions to:

select a plurality of sound samples from the audio data;

convert the plurality of sound samples into a series of coefficients representing frequency components of the audio data;

determine an expected rate of rotation for the tire based on the current speed of the vehicle;

in response to high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determine that the object is embedded in the tire; and

in response to a lack of high frequency components of the series of coefficients being modulated with respect to the expected rate of rotation, determine that no object is embedded in the tire.

17 . The computing device of claim 15 , wherein the processor is configured to determine that the object is embedded in the tire by executing further computer instructions to:

determine a rotation rate of the tire based on the speed and a circumference of the tire;

identify an audio anomaly in the audio data occurring at the determined rotation rate; and

determine that the object is embedded in the tire in response to identification of the audio anomaly.

18 . The computing device of claim 15 , wherein the processor is configured to determine that the object is embedded in the tire by executing further computer instructions to:

segment the audio data into data blocks whose size is calculated based on an expected length of time of a single rotation of the tire;

detect an audio anomaly in the audio data based on a degree of correlation between the data blocks; and

determine that the object is embedded in the tire based on the audio anomaly.

19 . The computing device of claim 15 , wherein the processor is configured to determine that the object is embedded in the tire by executing further computer instructions to:

perform a Pearson product-moment coefficient technique on the audio data to detect a time correlated audio anomaly indicative of the object being embedded in the tire.

20 . The computing device of claim 15 , wherein the processor is configured to determine that the object is embedded in the tire by executing further computer instructions to:

employ a trained machine learning model on the audio data to classify the audio data as containing a frequency dependent anomaly indicative of the object being embedded in the tire.