System and method for identifying at least one passenger of a vehicle by a pattern of movement

• US 10,220,854 B2
• Filed: 03/31/2017
• Issued: 03/05/2019
• Est. Priority Date: 01/20/2017
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Chen, Bonnie Hu
• Primary Examiner(s): Lee, Hsien Ming
• Application Number:
  US15/475,221
• Publication Number:
  US 20180208208A1
• Time in Patent Office: 704 Days
• Field of Search: 706 12, 706905, 706913
• CPC Class Codes: B60R 16/037 for occupant comfort , e.g. for automatic adjustment of appliances according to personal settings, e.g. seats, mirrors, steering wheel
• CPC Class Codes: B60W 2040/0809 Driver authorisation; Driver identity check
• CPC Class Codes: B60W 2040/0872 Driver physiology
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system and method for identifying at least one passenger of a vehicle by a pattern of movement that includes receiving at least one sensor signal from at least one wearable device. The system and method also include determining the pattern of movement based on data extracted from the at least one sensor signal and determining if the pattern of movement is consistent with at least one passenger movement pattern. Additionally, the system and method include identifying the at least one passenger of the vehicle based on the pattern of movement being consistent with the at least one passenger movement pattern. The system and method further include controlling at least one vehicle system by executing vehicle settings associated with the at least one passenger of the vehicle.

Claims

1. A computer-implemented method for identifying at least one passenger of a vehicle by a pattern of movement, comprising:

receiving sensor signals from at least one wearable device;

determining the pattern of movement based on data extracted from the sensor signals, wherein the extracted data represents physical movement and biosignal biometric parameters that are fused into combined movement data, wherein the combined movement data is converted into a data package that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle;

evaluating at least one data point that is extracted from the data package to determine if it is consistent with at least one data point programmed within and extracted from at least one predetermined passenger movement pattern, wherein the at least one passenger of the vehicle does not include a driver of the vehicle;

identifying the at least one passenger of the vehicle based on the at least one data point being consistent with the at least one predetermined passenger movement pattern;

executing a vehicle settings profile associated with the at least one passenger of the vehicle that is identified; and

controlling at least one vehicle system of the vehicle, wherein the at least one vehicle system is controlled based on the execution of the vehicle settings profile associated with the at least one passenger of the vehicle that is identified.

2. The computer-implemented method of claim 1, wherein the at least one predetermined passenger movement pattern includes a learned passenger movement pattern that is based on at least movement or gesture that occurs outside of the vehicle, wherein the learned passenger movement pattern is created by at least one approved passenger of the vehicle.

3. The computer-implemented method of claim 2, wherein the at least one predetermined passenger movement pattern includes a default passenger movement, wherein the default passenger movement pattern includes preprogrammed data that represents at least one movement that may be performed by the at least one passenger of the vehicle outside of the vehicle.

4. The computer-implemented method of claim 3, wherein the evaluating step to determine if it is consistent with the at least one predetermined passenger movement pattern includes evaluating and comparing the pattern of movement against the default passenger movement pattern and the learned passenger movement pattern.

5. The computer-implemented method of claim 4, wherein the evaluating step to determine if it is consistent with the at least one predetermined passenger movement pattern includes extracting at least one movement point from the data package that includes data that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle and comparing the at least one movement point extracted from the data package to at least one movement point extracted from the default passenger movement pattern and the learned passenger movement pattern.

6. The computer-implemented method of claim 5, wherein the evaluating step to determine if it is consistent with the at least one predetermined passenger movement pattern includes determining that there is a match between the at least one movement point extracted from the data package that includes the data that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle and the at least one movement extracted from the default passenger movement pattern or the learned passenger movement pattern.

7. The computer-implemented method of claim 1, wherein identifying the at least one passenger of the vehicle includes retrieving the vehicle settings profile linked with a device identification from the at least one wearable device that has provided the at least one sensor signal associated with the pattern of movement that is consistent with the at least one predetermined passenger movement pattern.

8. The computer-implemented method of claim 7, wherein identifying the at least one passenger of the vehicle includes classifying a perspective location or real time location of the at least one passenger within a non-driving area of the vehicle, wherein the perspective location or the real time location of the at least one passenger within the non-driving area of vehicle includes at least one of:

a non-driver passenger front seat, a left side passenger rear seat, a middle passenger rear seat, and a right side passenger rear seat.

9. The computer-implemented method of claim 8, wherein controlling the at least one vehicle system includes accessing and executing settings from the vehicle settings profile linked with the device identification of the wearable device and executing settings from the vehicle settings profile to control the at least one vehicle system located at a predetermined proximity to the perspective location or the real time location of the at least one passenger within the non-driving area of the vehicle.

10. A system for identifying at least one passenger of a vehicle by a pattern of movement, comprising:

a memory storing instructions when executed by a processor cause the processor to;

receive sensor signals from at least one wearable device;

determine the pattern of movement based on data extracted from the sensor signals, wherein the extracted data represents physical movement and biosignal biometric parameters that are fused into combined movement data, wherein the combined movement data is converted into a data package that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle;

evaluate at least one data point that is extracted from the data package to determine if it is consistent with at least one data point programmed within and extracted from at least one predetermined passenger movement pattern, wherein the at least one passenger of the vehicle does not include a driver of the vehicle;

identify the at least one passenger of the vehicle based on the at least one data point being consistent with the at least one predetermined passenger movement pattern;

execute a vehicle settings profile associated with the at least one passenger of the vehicle that is identified; and

control at least one vehicle system of the vehicle, wherein the at least one vehicle system is controlled based on the execution of the vehicle settings profile associated with the at least one passenger of the vehicle that is identified.

11. The system of claim 10, wherein the at least one predetermined passenger movement pattern includes a learned passenger movement pattern that is based on at least movement or gesture that occurs outside of the vehicle, wherein the learned passenger movement pattern is created by at least one approved passenger of the vehicle.

12. The system of claim 11, wherein the at east one predetermined passenger movement pattern includes a default passenger movement pattern, wherein the default passenger movement pattern includes preprogrammed data that represents at least one movement that may be performed by the at least one passenger of the vehicle outside of the vehicle.

13. The system of claim 12, wherein the evaluating step to determine if it is consistent with the at least one predetermined passenger movement pattern includes evaluating and comparing the pattern of movement against the default passenger movement pattern or the learned passenger movement pattern.

14. The system of claim 13, wherein evaluating the pattern of movement to determine if it is consistent with the at least one predetermined passenger movement pattern includes extracting at least one movement point from the data package that includes data that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle and comparing the at least one movement point extracted from the data package to at least one movement point extracted from the default passenger movement pattern and the learned passenger movement pattern.

15. The system of claim 14, wherein the evaluating step to determine if it is consistent with the at least one predetermined passenger movement pattern includes determining that there is a match between the at least one movement point extracted from the data package that includes the data that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle and the at least one movement point extracted from the default passenger movement pattern or the learned passenger movement pattern.

16. The system of claim 10, wherein identifying the at least one passenger of the vehicle includes retrieving the vehicle settings profile linked with a device identification from the at least one wearable device that has provided the at least one sensor signal associated with the pattern of movement that is consistent with the at least one predetermined passenger movement pattern.

17. The system of claim 16, wherein identifying the at least one passenger of the vehicle includes classifying a perspective location or real time location of the at least one passenger within a non-driving area of the vehicle, wherein the perspective location or the real time location of the at least one passenger within the non-driving area of vehicle includes at least one of:

a non-driver passenger front seat, a left side passenger rear seat, a middle passenger rear seat, and a right side passenger rear seat.

18. The system of claim 17, wherein controlling the at least one vehicle system includes accessing and executing settings from the vehicle settings profile linked with the device identification of the wearable device and executing settings from the vehicle settings profile to control the at least one vehicle system located at a predetermined proximity to the perspective location or the real time location of the at least one passenger within the non-driving area of the vehicle.

19. A non-transitory computer readable storage medium storing instructions that when executed by a computer, which includes at least a processor, causes the computer to perform a method, the method comprising:

receiving sensor signals from at least one wearable device;

determining a pattern of movement based on data extracted from the sensor signals, wherein the extracted data represents physical movement and biosignal biometric parameters that are fused into combined movement data, wherein the combined movement data is converted into a data package that is indicative of the pattern of movement with respect to at least one movement of the at least one passenger related to at least one non-driving area of the vehicle;

evaluating at least one data point that is extracted from the data package to determine if it is consistent with at least one data point programmed within and extracted from at least one predetermined passenger movement pattern, wherein the at least one passenger of the vehicle does not include a driver of the vehicle;

identifying the at least one passenger of the vehicle based on the at least one data point being consistent with the at least one predetermined passenger movement pattern;

executing a vehicle settings profile associated with the at least one passenger of the vehicle that is identified; and

controlling at least one vehicle system of the vehicle, wherein the at least one vehicle system is controlled based on the execution of the vehicle settings profile associated with the at least one passenger of the vehicle that is identified.

20. The non-transitory computer readable storage medium of claim 19, wherein controlling the at least one vehicle system includes accessing and executing settings from a vehicle settings profile linked with a device identification of the at least one wearable device and executing settings from the vehicle settings profile to control the at least one vehicle system located at a predetermined proximity to a perspective location or real time location of the at least one passenger within a non-driving area of the vehicle.