DETECTING THAT A MOBILE DEVICE IS RIDING WITH A VEHICLE

• US 20130245986A1
• Filed: 09/14/2012
• Published: 09/19/2013
• Est. Priority Date: 09/16/2011
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: GROKOP, Leonard Henry, DHINGRA, Bhuwan
• Granted Patent: US 10,539,586 B2
• Time in Patent Office: Days
• US Class Current: 702/141
• CPC Class Codes: G01P 13/00 Indicating or recording presence, absence, or direction, of movement electric switches H01H; counting moving objects G06M7/00
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: H04M 2250/12 including a sensor for measuring a physical value, e.g. temperature or motion telephones including GPS signal receivers H04M2250/10
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information

Abstract

Systems and methods herein enable a mobile device to detect that a user is traveling in association with a vehicle based at least on motion data. In some embodiments, accelerometer data is used. Motion data is leveraged in combination with various observations regarding vehicular movement to determine whether or not a mobile device is located in or on the vehicle. For instance, before entering the state of vehicular movement, it can be determined that the user is first in a walking state (e.g., walking to the car, bus, etc., and entering it). Likewise, after exiting the state of vehicular movement, the user re-enters the walking state (e.g., after stepping out of the car, bus, etc., the user again begins walking). Further, it can be determined that when the user is in the walking state, the accelerometer signals appear different to any accelerometer signals seen in the vehicular movement state.

Claims

1. A method comprising:

obtaining motion data from one or more motion-detecting devices; and

filtering the motion data to obtain present motion states for respective time intervals based on the motion data, the present motion states comprising one or more pedestrian motion states and one or more vehicular motion states, the one or more pedestrian motion states comprising a walk state, and the one or more vehicular motion states comprising a vehicular stop state;

wherein, during the filtering, transitions from the one or more pedestrian motion states to the one or more vehicular motion states are restricted to transitions from the walk state to the vehicular stop state and transitions from the one or more vehicular motion states to the one or more pedestrian motion states are restricted to transitions from the vehicular stop state to the walk state.

2. The method of claim 1 further comprising computing likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for the respective time intervals.

3. The method of claim 2 wherein computing the likelihoods comprises computing the likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for the respective time intervals according to a state machine that comprises the one or more pedestrian motion states and the one or more vehicular motion states.

4. The method of claim 3 wherein the state machine is an extended state machine comprising one or more sub-states for each of the one or more pedestrian motion states and the one or more vehicular motion states, and computing the likelihoods further comprises computing the likelihoods at least in part by advancing a device state through a plurality of sub-states of a motion state identified for the device state prior to transitioning the device state to a different motion state.

5. The method of claim 4 wherein a number of sub-states to be associated with each of the one or more pedestrian motion states and vehicular motion states is based at least in part on anticipated time spent in the respective motion states.

6. The method of claim 2 wherein the filtering comprises filtering the computed likelihoods using at least one of a forward-backward algorithm or a Viterbi algorithm.

7. The method of claim 2 wherein computing the likelihoods comprises:

obtaining sensor data from at least one of a Wi-Fi receiver, an audio input device or a GPS receiver; and

computing the likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for respective time intervals based on the motion data and the sensor data.

8. The method of claim 1 wherein the motion data is accelerometer data, and the one or more motion-detecting devices are one or more accelerometers.

9. The method of claim 8, wherein data from any inertial sensors other than the one or more accelerometers is omitted from the motion data.

10. The method of claim 8 wherein the filtering is based on a probabilistic model including the one or more pedestrian states and the one or more vehicular motion states.

11. The method of claim 1 wherein the filtering comprises:

identifying a first transition from the walk state to an interim state and a second transition from the interim state to one of at least one vehicular move states; and

classifying the interim state as the vehicular stop state.

12. The method of claim 1 wherein the filtering comprises:

buffering the motion data for a buffer time interval; and

obtaining the motion states for the respective time intervals based at least in part on the buffered motion data.

13. The method of claim 1 wherein the motion states comprise a sequence of most likely motion states for the respective time intervals.

14. The method of claim 1 wherein the motion states comprise estimated probabilities for respective motion states at the respective time intervals.

15. The method of claim 14 further comprising calculating a confidence score associated with a motion state at a given time interval by comparing two or more highest motion state probabilities at the given time interval.

16. The method of claim 15 wherein:

calculating the confidence score further comprises identifying a difference between a highest motion state probability and a second highest motion state probability at the given time interval; and

the method further comprises;

comparing the difference to a confidence threshold; and

if the difference is less than the confidence threshold, substituting the motion states for the given time interval with at least one of a default state or an unknown state.

17. A mobile device comprising:

one or more motion detectors configured to generate motion data;

a state machine module comprising one or more pedestrian motion states and one or more vehicular motion states, the one or more pedestrian motion states comprising a walk state and the one or more vehicular motion states comprising a vehicular stop state and at least one vehicular move state;

a filtering module communicatively coupled to the state machine module and configured to filter the motion data to obtain present motion states for respective time intervals based on the motion data,

wherein, within the state machine module, transitions from the one or more pedestrian motion states to the one or more vehicular motion states are restricted to transitions from the walk state to the vehicular stop state and transitions from the one or more vehicular motion states to the one or more pedestrian motion states are restricted to transitions from the vehicular stop state to the walk state.

18. The mobile device of claim 17 further comprising a likelihood computation module communicatively coupled to the one or more motion detectors, the state machine module and the filtering module and configured to compute likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for the respective time intervals.

19. The mobile device of claim 18 wherein the state machine is an extended state machine comprising one or more sub-states for each of the one or more pedestrian motion states and the one or more vehicular motion states, and the likelihood computation module is further configured to compute the likelihoods at least in part by advancing a device state through a plurality of sub-states of a motion state identified for the device state prior to transitioning the device state to a different motion state.

20. The mobile device of claim 19 wherein a number of sub-states to be associated with each of the one or more pedestrian motion states and vehicular motion states is based at least in part on anticipated time spent in the respective motion states.

21. The mobile device of claim 18 wherein the filtering module is further configured to filter the computed likelihoods using at least one of a forward-backward algorithm or a Viterbi algorithm.

22. The mobile device of claim 18 wherein the likelihood computation module is further configured to:

obtain sensor data from at least one of a Wi-Fi receiver, an audio input device or a GPS receiver; and

compute the likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for respective time intervals based on the motion data and the sensor data.

23. The mobile device of claim 17 wherein the one or more motion detectors is one or more accelerometers and the motion data is accelerometer data.

24. A mobile device comprising:

means for obtaining motion data from one or more means for detecting motion; and

means for filtering the motion data to obtain present motion states for respective time intervals based on the motion data, the present motion states comprising one or more pedestrian motion states and one or more vehicular motion states, the one or more pedestrian motion states comprising a walk state, and the one or more vehicular motion states comprising a vehicular stop state;

wherein, within the means for filtering, transitions from the one or more pedestrian motion states to the one or more vehicular motion states are restricted to transitions from the walk state to the vehicular stop state and transitions from the one or more vehicular motion states to the one or more pedestrian motion states are restricted to transitions from the vehicular stop state to the walk state.

25. The mobile device of claim 24, further comprising means for computing likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for the respective time intervals.

26. The mobile device of claim 25 wherein the means for filtering comprises means for filtering the computed likelihoods using at least one of a forward-backward algorithm or a Viterbi algorithm.

27. The mobile device of claim 25 further comprising:

means for obtaining sensor data from at least one of a Wi-Fi receiver, an audio input device or a GPS receiver; and

means for computing the likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for respective time intervals based on the motion data and the sensor data.

28. The mobile device of claim 24, wherein the motion data is accelerometer data, and the one or more means for detection motion comprise one or more accelerometers.

29. A non-transitory processor-readable medium comprising processor-readable instructions configured to cause a processor to:

obtain motion data from one or more motion-detecting devices; and

filter the motion data to obtain present motion states for respective time intervals based on the motion data, the present motion states comprising one or more pedestrian motion states and one or more vehicular motion states, the one or more pedestrian motion states comprising a walk state, and the one or more vehicular motion states comprising a vehicular stop state;

wherein, during the filtering, transitions from the one or more pedestrian motion states to the one or more vehicular motion states are restricted to transitions from the walk state to the vehicular stop state and transitions from the one or more vehicular motion states to the one or more pedestrian motion states are restricted to transitions from the vehicular stop state to the walk state.

30. The processor-readable medium of claim 29, wherein the instructions are further configured to cause the processor to compute likelihoods for the one or more pedestrian motion states and the one or more vehicular motion states for the respective time intervals.