SYSTEMS AND METHODS FOR THE DETECTION OF IMPLICIT GESTURES

• US 20150286952A1
• Filed: 04/03/2014
• Published: 10/08/2015
• Est. Priority Date: 04/03/2014
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: El Dokor, Tarek A., King, Joshua, Cluster, Jordan, Holmes, James E.
• Granted Patent: US 9,342,797 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Some embodiments provide systems and methods for enabling a learning implicit gesture control system for use by an occupant of a vehicle. The method includes identifying features received from a plurality of sensors and comparing the features to antecedent knowledge stored in memory. A system output action that corresponds to the features can then be provided in the form of a first vehicle output. The method further includes detecting a second vehicle output from the plurality of sensors and updating the antecedent knowledge to associate the system output action with the second vehicle output.

Claims

1. A method for enabling a learning implicit gesture control by an occupant of a vehicle, comprising:

identifying a first set of features in a plurality of inputs received from a plurality of sensors;

comparing the first set of features to an antecedent knowledge stored in a memory device to identify a system output action corresponding to the first set of features and a first vehicle output, the system output action being configured to cause a first vehicle output;

detecting a second vehicle output from the plurality of inputs; and

updating the antecedent knowledge to associate the system output action with the second vehicle output.

2. The method of claim 1, including only updating the antecedent knowledge when the second vehicle output occurs within a pre-determined time period of the system output action being identified.

3. The method of claim 1, wherein the antecedent knowledge includes a plurality of previously extracted and stored features associated with a given system output.

4. The method of claim 1, wherein the antecedent knowledge includes knowledge stored from deep learning through a generalist network, a specialist network, and a monitor network.

5. The method of claim 1, wherein the plurality of sensors include visual sensors.

6. The method of claim 1, wherein the plurality of sensors include audio sensors.

7. The method of claim 1, wherein the system output action includes modifying a status of at least one of a plurality of systems of the vehicle.

8. The method of claim 7, wherein the plurality of systems of the vehicle include at least one of a reading light, a navigation system, and a telecommunications system.

9. A method for enabling learning implicit gesture control by an occupant of a vehicle, comprising:

identifying a first set of features in a plurality of inputs received from a plurality of sensors;

inputting the first set of features to a deep knowledge learning system stored in a memory device to identify a system output action corresponding to the first set of features and a first vehicle output, the system output action being configured to cause a first vehicle output;

detecting a second vehicle output from the plurality of inputs; and

when the second vehicle output occurs within a pre-determined time period of the system output action being generated, performing a self-healing action to update the antecedent knowledge.

10. The method of claim 9, wherein the self-healing action includes updating the antecedent knowledge to associate the system output action with the second vehicle output.

11. The method of claim 10, including, before updating the antecedent knowledge, prompting the user to confirm that the system output action should be associated with the second vehicle output.

12. The method of claim 9, wherein the self-healing action includes distinguishing the first set of features from a second set of features in the antecedent knowledge, the second set of features in the antecedent knowledge being associated with the system output action configured to cause the first vehicle output.

13. An implicit gesture learning system, the system comprising:

a processor, the processor in communication with a plurality of sensors coupled to a vehicle;

the processor being configured to perform the steps of;

identifying a first set of features in a plurality of inputs received from the plurality of sensors;

comparing the first set of features to an antecedent knowledge stored in a memory device to identify a system output action corresponding to the first set of features and a first vehicle output, the system output action being configured to cause a first vehicle output;

detecting a second vehicle output from the plurality of inputs; and

updating the antecedent knowledge to associate the system output action with the second vehicle output.

14. The system of claim 13, wherein the processor is configured to only update the antecedent knowledge when the second vehicle output occurs within a pre-determined time period of the system output action being generated.

15. The system of claim 13, wherein the antecedent knowledge includes a plurality of previously extracted and stored features associated with a given system output.

16. The system of claim 13, wherein the antecedent knowledge includes knowledge stored from deep learning through a generalist network, a specialist network, and a monitor network.

17. The system of claim 13, wherein the plurality of sensors include visual sensors.

18. The system of claim 13, wherein the plurality of sensors include audio sensors.

19. The system of claim 13, wherein the system output action includes modifying a status of at least one of a plurality of systems of the vehicle.

20. The system of claim 19, wherein the plurality of systems of the vehicle include at least one of a reading light, a navigation system, and a telecommunications system.