Systems and methods for detecting vehicle door closing events

Patent metadata

Field	Value
Publication number	US-11312213-B2
Application number	US-201916712612-A
Country	US
Kind code	B2
Filing date	Dec 12, 2019
Priority date	Jun 26, 2019
Publication date	Apr 26, 2022
Grant date	Apr 26, 2022

Abstract

The disclosed computer-implemented method may include receiving sensor data associated with a mobile device associated with a vehicle, wherein the sensor data includes at least one of an angular velocity vector, a linear acceleration vector, and a rotational acceleration vector recorded over a period of time, determining an event signature based on the sensor data, and detecting a door closing event associated with the vehicle based at least in part on the event signature. Other methods, systems, and computer-readable media are disclosed.

First claim

What is claimed is:

1. A computer-implemented method comprising:
identifying linear acceleration signals as a function of time from one or more sensors corresponding to a mobile device mounted to an interior cabin portion of a vehicle;
utilizing a door-closing model to determine a door-closing event
of a door from a plurality of doors associated with the vehicle based on the linear acceleration signals; and
generating at least one of a transportation-initiation signal or a transportation-completion signal based on the door-closing event.

2. The computer-implemented method of claim 1 , further comprising:
identifying rotational velocity signals as a function of time from the one or more sensors corresponding to the mobile device; and
utilizing the door-closing model to determine the door-closing event further based on the rotational velocity signals.

3. The computer-implemented method of claim 1 , further comprising:
identifying rotational acceleration signals as a function of time from the one or more sensors corresponding to the mobile device; and
utilizing the door-closing model to determine the door-closing event further based on the rotational acceleration signals.

4. The computer-implemented method of claim 1 , further comprising:
identifying, from one or more additional sensors associated with the mobile device or a transportation provider computing device separate from the mobile device, at least one of:
sound level data based on sound levels recorded inside the vehicle;
relative air pressure level data based on an ambient air pressure recorded inside the vehicle; and
image data based on images recorded inside the vehicle.

5. The computer-implemented method of claim 4 , further comprising utilizing the door-closing model to determine the door-closing event based on at least one of the sound level data, the relative air pressure level data, or the image data.

6. The computer-implemented method of claim 1 , wherein utilizing the door-closing model to determine the door-closing event comprises utilizing a machine-learning model trained to predict door-closing events.

7. The computer-implemented method of claim 1 , further comprising transmitting, based on the door-closing event, a control signal to a door lock of one or more doors of the plurality of doors to cause the door lock to engage or disengage for controlling access into or out of the vehicle.

8. The computer-implemented method of claim 1 , further comprising:
identifying, via a graphical user interface of a provider computing device, a user interaction with a digital icon to provide an initial signal indicating a transportation pickup or a transportation drop-off, and
wherein generating the transportation-initiation signal or the transportation-completion signal based on the door-closing event comprises modifying or replacing a digital record based on the initial signal with respect to at least one of a time or location associated with the transportation pickup or the transportation drop-off.

9. The computer-implemented method of claim 1 , further comprising:
identifying, utilizing a global positioning system, a location of the vehicle at a time corresponding to the door-closing event; and
associating the location of the vehicle with a transportation pickup or a transportation drop-off.

10. The computer-implemented method of claim 1 , wherein determining the door-closing event based on the linear acceleration signals comprises:
processing the linear acceleration signals in a frequency domain; and
assigning weights to each of three dimensions of the linear acceleration signals, wherein each of the three dimensions of the linear acceleration signals correspond to three-dimensional movement of the mobile device and the assigned weights are based on a relative orientation of the mobile device to the vehicle.

11. A system comprising:
one or more memory devices; and
one or more processors configured to cause the system to:
identify linear acceleration signals as a function of time from one or more sensors corresponding to a mobile device mounted to an interior cabin portion of a vehicle;
utilize a door-closing model to determine a door-closing event
of a door from a plurality of doors associated with the vehicle based on the linear acceleration signals; and
generate at least one of a transportation-initiation signal or a transportation-completion signal based on the door-closing event.

12. The system of claim 11 , wherein the one or more processors are configured to cause the system to:
identify rotational velocity signals as a function of time from the one or more sensors corresponding to the mobile device; and
utilize the door-closing model to determine the door-closing event further based on the rotational velocity signals.

13. The system of claim 11 , wherein the one or more processors are configured to cause the system to:
identify rotational acceleration signals as a function of time from the one or more sensors corresponding to the mobile device; and
utilize the door-closing model to determine the door-closing event further based on the rotational acceleration signals.

14. The system of claim 11 , wherein the one or more processors are configured to cause the system to:
identify, from one or more additional sensors associated with the mobile device or a transportation provider computing device separate from the mobile device, at least one of:
sound level data based on sound levels recorded inside the vehicle;
relative air pressure level data based on an ambient air pressure recorded inside the vehicle; or
image data based on images recorded inside the vehicle.

15. The system of claim 14 , wherein the one or more processors are configured to cause the system to utilize the door-closing model to determine the door-closing event based on at least one of the sound level data, the relative air pressure level data, or the image data.

16. The system of claim 11 , wherein the one or more processors are configured to cause the system to utilize the door-closing model to determine the door-closing event by utilizing a machine-learning model trained to predict door-closing events.

17. The system of claim 11 , wherein the one or more processors are configured to cause the system to transmit, based on the door-closing event, a control signal to a door lock of one or more doors of the plurality of doors to cause the door lock to engage or disengage for controlling access into or out of the vehicle.

18. The system of claim 11 , wherein the one or more processors are configured to cause the system to:
identify, via a graphical user interface of a provider computing device, a user interaction with a digital icon to provide an initial signal indicating a transportation pickup or a transportation drop-off, and
generate the transportation-initiation signal or the transportation-completion signal based on the door-closing event by modifying or replacing a digital record based on the initial signal with respect to at least one of a time or location associated with the transportation pickup or the transportation drop-off.

19. The system of claim 11 , wherein the one or more processors are configured to cause the system to:
identify, utilizing a global positioning system, a location of the vehicle at a time corresponding to the door-closing event; and
associate the location of the vehicle with a transportation pickup or a transportation drop-off.

20. A non-transitory computer-readable storage medium comprising computer

Assignees

• Lyft Inc

Inventors

• Dicarlo Michael Jonathan
• Clement Romain
• Thor Mikael Kjell Anders

Classifications

• E05Y2400/37

  by using acceleration sensors · CPC title

• E05Y2400/8515

  Smart phones; Tablets · CPC title

• E05Y2400/44

  Sensors not directly associated with the wing movement · CPC title

• E05Y2400/36

  Speed control, detection or monitoring · CPC title

• E05F15/42

  Detection using safety edges · CPC title