SYSTEMS, METHODS, AND APPARATUS FOR LEARNING THE IDENTITY OF AN OCCUPANT OF A VEHICLE

• US 20140266623A1
• Filed: 12/29/2011
• Published: 09/18/2014
• Est. Priority Date: 12/29/2011
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Graumann, David L., Healey, Jennifer, Montesinos, Carlos
• Application Number:
  US13/977,613
• Publication Number:
  US 20140266623A1
• Time in Patent Office: Days
• US Class Current: 340/10.1
• CPC Class Codes: B60K 28/02 responsive to conditions relating to the driver
• CPC Class Codes: B60K 28/066 actuating a signalling device B60K28/063 takes precedence
• CPC Class Codes: B60N 2/0248 with memory of positions
• CPC Class Codes: B60N 2210/24 Cameras
• CPC Class Codes: B60N 2210/40 Force or pressure sensors
• CPC Class Codes: B60N 2220/20 using a deterministic algorithm
• CPC Class Codes: B60N 2230/20 Wireless data transmission
• CPC Class Codes: B60R 25/25 using biometry
• CPC Class Codes: B60R 25/305 using a camera
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: G06K 7/10 by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation

Abstract

Certain embodiments of the invention may include systems, methods, and apparatus for learning the identity of an occupant of a vehicle. According to an example embodiment of the invention, a method is provided for learning an identity of an occupant of a vehicle. The method includes receiving a primary identification (ID) input and one or more secondary ID inputs, wherein the primary ID input includes identification token information; retrieving cluster information based at least in part on the primary ID input; comparing the one or more secondary ID inputs with the cluster information; determining a confidence value based at least in part on the comparison of the one or more secondary ID inputs with the cluster information; training the cluster information based at least in part on the received one or more secondary ID inputs; and storing the trained cluster information.

Claims

1. A method comprising executing computer-executable instructions by one or more processors for learning an identity of an occupant of a vehicle, the method further comprising:

receiving a primary identification (ID) input and one or more secondary ID inputs, wherein the primary ID input comprises identification token information;

retrieving cluster information based at least in part on the primary ID input;

comparing the one or more secondary ID inputs with the cluster information;

determining a confidence value based at least in part the comparison of the one or more secondary ID inputs with the cluster information;

training the cluster information based at least in part on the received one or more secondary ID inputs; and

storing the trained cluster information.

2. The method of claim 1, wherein the identification token information comprises information stored on one or more of a radio frequency identification (RFID) tag, a bar code, a magnetic strip, a key fob, or a non-volatile memory.

3. The method of claim 1, wherein the secondary ID inputs comprise one or more of:

weight, weight distribution, image features, or audible features associated with the occupant.

4. The method of claim 1, wherein the cluster information comprises an indication of prior association between the primary ID input and the one or more secondary ID inputs.

5. The method of claim 4, wherein the indication comprises one or more degrees of relative association.

6. The method of claim 1, further comprising outputting information based at least in part on the comparing of the one or more secondary ID inputs with the cluster information.

7. The method of claim 1, wherein training the cluster information is further based at least in part on the determined confidence value.

8. The method of claim 1, wherein training the cluster information comprises updating a mean and variance of the cluster information based at least in part on one or more of the received secondary ID inputs.

9. A vehicle comprising:

a primary reader for receiving input from a primary identification (ID) device;

one or more secondary ID input devices;

at least one memory for storing data and computer-executable instructions; and

one or more processors configured to access the at least one memory and further configured to execute computer-executable instructions for;

receiving a primary ID input from the primary reader and one or more secondary ID inputs from the one or more secondary ID input devices;

retrieving cluster information from the at least one memory associated with the vehicle based at least in part on the primary ID input;

comparing the one or more secondary ID inputs with the cluster information;

determining a confidence value based at least in part on the cluster information or on the comparison of the one or more secondary ID inputs with the cluster information; and

training the cluster information based at least in part on e received one or more secondary ID inputs.

10. The vehicle of claim 9, further comprising at east a speaker or a display for prompting a occupant of the vehicle.

11. The vehicle of claim 9, wherein the primary ID device comprises information stored on one or more of a radio frequency identification (RFID) tag, a bar code, a magnetic strip, or a non-volatile memory.

12. The vehicle of claim 9, wherein the one or more secondary ID input devices comprise one or more of:

sensors for measuring weight or weight distribution associated with a occupant of the vehicle, a camera for capturing image features associated with an occupant of the vehicle, or a microphone for cape g audible features associated with the occupant.

13. The vehicle of claim 9, wherein the cluster information comprises an indication of prior association between the primary ID input and the one or more secondary ID inputs.

14. The vehicle of claim 13, wherein the indication comprises one or more degrees of relative association.

15. The vehicle of claim 9, wherein the one or more processors are further configured for outputting information based at least in part on the comparing of the one or more secondary ID inputs with the cluster information.

16. The vehicle of claim 9, wherein training the cluster information is further based at least part on the determined confidence value.

17. The vehicle of claim 9, wherein training the cluster information comprises updating a mean and variance of the cluster information based at least in part on one or more of the received secondary ID inputs.

18. An apparatus comprising:

at least one memory for storing data and computer-executable instructions; and

one or more processors configured to access the at least one memory and further configured to execute computer-executable instructions for;

receiving a primary identification (ID) input and one or more secondary ID inputs;

retrieving cluster information from the at east one memory based at least in part on the primary ID input;

comparing the one or more secondary ID inputs with the cluster information;

determining a confidence value based at least in part on the cluster information or on the comparison of the one or more secondary ID inputs with the cluster information; and

training the cluster information based at least in part on the received one or more secondary ID inputs.

19. The apparatus of claim 18, wherein the primary ID input comprises information stored on one or more of a radio frequency identification (RFID) tag, a bar code, a magnetic strip, a key fob, or a non-volatile memory.

20. The apparatus of claim 18, wherein the secondary ID inputs comprise one or more of:

weight or weight distribution associated with an occupant of a vehicle, image features associated with the occupant of the vehicle, or audible features associated with the occupant of the vehicle.

21. The apparatus of claim 18, wherein the cluster information comprises an indication of prior association between the primary ID input and the one or more secondary ID inputs, wherein the indication comprises one or more degrees of relative association.

22. The apparatus of claim 18, wherein the one or more processors are further configured for outputting information based at least in part on the comparing of the one or more secondary ID inputs with the cluster information.

23. The apparatus of claim 18, wherein training the cluster information is further based at least in part on the determined confidence value.

24. The apparatus of claim 18, wherein training the cluster information comprises updating a mean and variance of the cluster information based at least in part on one or more of the received secondary ID inputs.

25. A computer program product, comprising a computer-usable medium having a computer-readable program code embodied therein, said computer-readable program code adapted to be executed to implement a method for learning an identity of an occupant of a vehicle, the method further comprising:

receiving a primary identification (ID) input and one or more secondary ID inputs;

retrieving cluster information based at least on the primary ID input;

comparing the one or more secondary ID inputs with the cluster information;

determining a confidence value based at least in part on the cluster information or on the comparison of the one or more secondary ID inputs with the cluster information; and

training the cluster information information based at least in part on the received one or more secondary ID inputs.

26. The computer program product of claim 25, wherein the primary ID input comprises information stored on one or more of a radio frequency identification (RFID) tag, a bar code, a magnetic strip, a key fob, or a non-volatile memory, and wherein the secondary ID inputs comprise one or more of weight or weight distribution associated with an occupant of a vehicle, image features associated with the occupant of the vehicle, or audible features associated with the occupant of the vehicle.

27. The computer program product of claim 25, wherein the cluster information comprises an indication of prior association between the primary ID input and the one or more secondary ID inputs.

28. The computer program product of claim 25, further comprising outputting information based at least in part on the comparing of the one or more secondary ID inputs with the cluster information.

29. The computer program product of claim 25, wherein training the cluster information is further based at least in part on the determined confidence value.

30. The computer program product of claim 25, wherein training the cluster information comprises updating a mean and variance of the cluster information based at least in part on one or more of the received secondary ID inputs.