Determining a location of a squeak or rattle associated with a vehicle

1. A computer-implemented method, comprising:

detecting, using a set of one or more microphones, a sound associated with a vehicle;

analyzing, using one or more machine learning models, to determine whether the sound associated with the vehicle is a squeak or a rattle, wherein at least one of the one or more machine learning models is trained using a plurality of sounds emitted in relation to one or more vehicles;

determining, using the one or more machine learning models, a location associated with the squeak or the rattle;

maintaining a count of detection of the squeak or the rattle at the location; and

triggering an action when the count of detections at the location exceeds a minimum threshold, wherein the action comprises outputting an alert to a human machine interface in the vehicle that instructs an occupant to stop the vehicle, lower a speed of the vehicle, or service the vehicle soon, wherein the alert is displayed on a system-generated graphical user interface, indicates that the sound associated with the vehicle is the squeak or the rattle, and includes the location of the squeak or the rattle.

2. The computer-implemented method of claim 1 , wherein the location is determined based on detecting different sound levels between a first microphone and a second microphone of the set of one or more microphones.

3. The computer-implemented method of claim 1 , wherein the location is determined based on detecting a time difference between a first microphone of the set of one or more microphones receiving the sound and a second microphone of the set of one or more microphones receiving the sound.

4. The computer-implemented method of claim 1 , wherein the location is determined based on correlating the sound to a sound signature associated with a part of the vehicle.

5. The computer-implemented method of claim 1 , further comprising:

triggering a second action when the location is associated with a first part of the vehicle; and

preventing the second action from triggering when the location is associated with a second part of the vehicle.

6. The computer-implemented method of claim 1 , further comprising:

determining a lower severity or a higher severity based on the location;

generating an on-board diagnostics code when determining the lower severity; and

outputting a message to a human machine interface (HMI) in the vehicle when determining the higher severity.

7. The computer-implemented method of claim 1 , further comprising:

determining a severity level based on the location of the squeak or the rattle, wherein the severity level is lower when the location is associated with an interior trim of the vehicle than when the location is associated with a frame of the vehicle.

8. The computer-implemented method of claim 1 , wherein the action further comprises:

determining a part number associated with a part of the vehicle based on the location.

9. The computer-implemented method of claim 1 , wherein at least one of the one or more machine learning models is trained by playing squeaks and rattles through speakers configured in the one or more vehicles.

10. The computer-implemented method of claim 9 , wherein the squeaks and rattles played through the speakers are generated synthetically by applying a force to one or more components of a vehicle to cause the one or more components to move in a manner that emits a sound.

11. A system, comprising:

a set of one or more microphones configured in relation to a vehicle;

a memory; and

a processor configured to execute instructions stored in the memory to:

detect, using the set of one or more microphones, a sound associated with the vehicle;

analyze, using one or more machine learning models, the sound associated with the vehicle to determine whether the sound associated with the vehicle is a squeak or a rattle, wherein at least one of the one or more machine learning models is trained using a plurality of sounds emitted in relation to one or more vehicles;

determine, using the one or more machine learning models, a location associated with the squeak or the rattle;

determine a severity level based on the location of the squeak or the rattle, wherein the severity level is lower when the location is associated with an interior trim of the vehicle than when the location is associated with a frame of the vehicle; and

trigger an action based on the severity level, wherein the action comprises outputting an alert to a human machine interface in the vehicle that instructs an occupant to stop the vehicle, lower a speed of the vehicle, or service the vehicle soon, wherein the alert is displayed on a system-generated graphical user interface, indicates that the sound associated with the vehicle is the squeak or the rattle, and includes the location of the squeak or the rattle.

12. The system of claim 11 , wherein the location is determined based on detecting different sound levels between a first microphone and a second microphone of the set of one or more microphones.

13. The system of claim 11 , wherein the location is determined based on detecting a time difference between a first microphone of the set of one or more microphones receiving the sound and a second microphone of the set of one or more microphones receiving the sound.

14. The system of claim 11 , wherein the location is determined based on correlating the sound to a sound signature associated with a part of the vehicle.

15. The system of claim 11 , wherein the processor is further configured to execute instructions stored in the memory to:

maintain a count of detections of the squeak or the rattle at the location; and

trigger the action when the count of detections exceeds a minimum threshold.

16. The system of claim 11 , wherein the processor is further configured to execute instructions stored in the memory to:

trigger a second action when the location is associated with a first part of the vehicle; and

exclude triggering the second action when the location is associated with a second part of the vehicle.

17. The system of claim 11 , wherein the processor is further configured to execute instructions stored in the memory to:

generate an on-board diagnostics code; and

store the on-board diagnostics code in a data structure, wherein the on-board diagnostics code is accessible by a repair technician.

18. A vehicle, comprising:

an accessory loop;

a set of one or more microphones; and

one or more processors that execute computer-readable instructions that cause the one or more processors to:

detect, using the set of one or more microphones, a sound associated with the vehicle;

analyze, using one or more machine learning models, the sound associated with the vehicle to determine whether the sound associated with the vehicle is a squeak or a rattle, wherein at least one of the machine learning models is trained using a plurality of sounds emitted in relation to one or more vehicles;

determine, using the one or more machine learning models, a location associated with the squeak or the rattle;

maintain a count of detections of the squeak or the rattle at the location; and

trigger an action when the count of detections at the location exceeds a minimum threshold, wherein the action comprises outputting an alert to a human machine interface in the vehicle that instructs an occupant to stop the vehicle, lower a speed of the vehicle, or service the vehicle soon, wherein the alert is displayed on a system-generated graphical user interface, indicates that the sound associated with the vehicle is the squeak or the rattle, and includes the location of the squeak or the rattle.

19. The vehicle of claim 18 , wherein the vehicle is an electric vehicle having an accessory loop, and the action uses the accessory loop when triggered.

20. The vehicle of claim 18 , wherein the set of one or more microphones comprises microphones that vary in detection capability.