METHODS AND SYSTEMS FOR SENSOR-BASED VEHICLE ACCELERATION DETERMINATION

• US 20160325756A1
• Filed: 05/09/2016
• Published: 11/10/2016
• Est. Priority Date: 05/07/2015
• Status: Active Grant

• Current Assignee: Cambridge Mobile Telematics, Inc.
• Original Assignee: Truemotion, Inc.
• Inventors: Cordova, Brad, Sahoo, Sanujit
• Granted Patent: US 10,067,157 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 21/185 for gravity
• CPC Class Codes: G01P 21/00 Testing or calibrating of apparatus or devices covered by the preceding groups
• CPC Class Codes: G01S 19/14 specially adapted for specific applications
• CPC Class Codes: H04M 2250/12 including a sensor for measuring a physical value, e.g. temperature or motion

Abstract

Methods and systems are provided that enable accurate driving behavior data (e.g., vehicle acceleration data) to be obtained by a mobile device, despite the reference frames of the mobile device and the vehicle occasionally moving relative to each other. Accordingly, a user does not have to maintain a mobile device stationary relative to a vehicle in order to have a high likelihood that accurate driving data is collected.

Claims

1. A system comprising:

a mobile device comprising a plurality of sensors including an accelerometer;

a memory; and

a processor coupled to the memory, wherein the processor is configured to perform operations including;

obtaining a plurality of movement measurements over a plurality of time periods from the mobile device in a vehicle;

extracting a first subset of movement measurements from the plurality of movement measurements corresponding to a first time period of the plurality of time periods;

determining a first gravity vector associated with the first time period using at least two movement measurements of the first subset of movement measurements;

extracting a second subset of movement measurements from the plurality of movement measurements corresponding to a second time period of the plurality of time periods;

determining a second gravity vector associated with the second time period using at least two movement measurements of the second subset of movement measurements;

determining a gravity angle difference or a gravity magnitude difference between the first gravity vector and the second gravity vector;

determining that the gravity angle difference is above a first threshold or that the gravity magnitude difference is above a second threshold;

classifying the first subset of movement measurements and the second subset of movement measurements as movements of the mobile device with respect to the vehicle;

removing the first subset of movement measurements for the first time period and the second subset of movement measurements for the second time period from the plurality of movement measurements to produce remaining movement measurements over remaining time periods; and

determining at least one acceleration measurement for the vehicle using the remaining movement measurements over the remaining time periods.

2. The system of claim 1, wherein at least one of the plurality of movement measurements is obtained using the accelerometer of the mobile device.

3. The system of claim 1, wherein the plurality of sensors further comprises a gyroscope, and wherein at least one of the plurality of movement measurements is obtained using the gyroscope of the mobile device.

4. The system of claim 1, wherein at least one of the first gravity vector and the second gravity vector are determined using a quaternion value.

5. The system of claim 4, wherein the quaternion value is calculated using a Kalman matrix.

6. The system of claim 1, wherein the operations further include:

determining a third gravity vector using the remaining movement measurements;

separating the at least one acceleration measurement into first, second and third components;

identifying the first component as a vertical acceleration component, wherein the first component is more highly correlated with acceleration projected onto the third gravity vector than the second component or the third component;

identifying the second component as a lateral acceleration component, wherein the second component is more highly correlated with a rotation rate around the third gravity vector than the third component; and

identifying the third component as a longitudinal acceleration component.

7. The system of claim 6, wherein the at least one acceleration measurement is separated into first, second and third components by applying principle component analysis.

8. The system of claim 6, wherein the operations further include:

determining a correlation coefficient for the lateral acceleration component as compared to the third gravity vector;

determining that a sign of the correlation coefficient is negative; and

reversing the sign of the lateral acceleration component.

9. The system of claim 1, wherein the at least one acceleration measurement comprises a first acceleration measurement for a first subset of the remaining time periods and a second acceleration measurement for a second subset of the remaining time periods, and wherein the operations further include:

separating the first acceleration measurement into first, second and third components;

separating the second acceleration measurement into fourth, fifth and sixth components;

determining a first mean value for the first component and the fourth component, a second mean value for the second component and the fifth component, and a third mean value for the third component and the sixth component;

determining a first difference between the first mean value and the first component, a second difference between the first mean value and the fourth component, a third difference between the second mean value and the second component, a fourth difference between the second mean value and the fifth component, a fifth difference between the third mean value and the third component, and a sixth difference between the third mean value and the sixth component;

determining a covariance matrix using the first difference, the second difference, the third difference, the fourth difference, the fifth difference and the sixth difference;

determining a plurality of eigenvectors and a plurality of eigenvalues of the covariance matrix;

selecting a first, second and third eigenvalue based on the largest eigenvalues of the plurality of eigenvalues;

identifying first, second and third eigenvectors corresponding to the first, second and third eigenvalues; and

associating the first, second and third eigenvectors with a vertical acceleration component, a lateral acceleration component, or a longitudinal acceleration component.

10. A method comprising:

obtaining a plurality of movement measurements over a plurality of time periods from a mobile device in a vehicle;

extracting a first subset of movement measurements from the plurality of movement measurements corresponding to a first time period of the plurality of time periods;

determining a first gravity vector associated with the first time period using at least two movement measurements of the first subset of movement measurements;

extracting a second subset of movement measurements from the plurality of movement measurements corresponding to a second time period of the plurality of time periods;

determining a second gravity vector associated with the second time period using at least two movement measurements of the second subset of movement measurements;

determining a gravity angle difference or a gravity magnitude difference between the first gravity vector and the second gravity vector;

determining that the gravity angle difference is above a first threshold or that the gravity magnitude difference is above a second threshold;

classifying the first subset of movement measurements and the second subset of movement measurements as movements of the mobile device with respect to the vehicle,

removing the first subset of movement measurements for the first time period and the second subset of movement measurements for the second time period from the plurality of movement measurements to produce remaining movement measurements over remaining time periods; and

determining at least one acceleration measurement for the vehicle using the remaining movement measurements over the remaining time periods.

11. The method of claim 10, wherein at least one of the plurality of movement measurements is obtained using an accelerometer of the mobile device.

12. The method of claim 10, wherein at least one of the plurality of movement measurements is obtained using a gyroscope of the mobile device.

13. The method of claim 10, wherein at least one of the first gravity vector and the second gravity vector are determined using a quaternion value.

14. The method of claim 13, wherein the quaternion value is calculated using a Kalman matrix.

15. The method of claim 10, further comprising:

determining a third gravity vector using the remaining movement measurements;

separating the at least one acceleration measurement into first, second and third components;

identifying the first component as a vertical acceleration component, wherein the first component is more highly correlated with acceleration projected onto the third gravity vector than the second component or the third component;

identifying the second component as a lateral acceleration component, wherein the second component is more highly correlated with a rotation rate around the third gravity vector than the third component; and

identifying the third component as a longitudinal acceleration component.

16. The method of claim 15, wherein the at least one acceleration measurement is separated into first, second and third components by applying principle component analysis.

17. The method of claim 15, further comprising:

determining a correlation coefficient for the lateral acceleration component as compared to the third gravity vector;

determining that a sign of the correlation coefficient is negative; and

reversing the sign of the lateral acceleration component.

18. The method of claim 10, wherein the at least one acceleration measurement comprises a first acceleration measurement for a first subset of the remaining time periods and a second acceleration measurement for a second subset of the remaining time periods, and wherein the method further comprises:

separating the first acceleration measurement into first, second and third components;

separating the second acceleration measurement into fourth, fifth and sixth components;

determining a first mean value for the first component and the fourth component, a second mean value for the second component and the fifth component, and a third mean value for the third component and the sixth component;

determining a first difference between the first mean value and the first component, a second difference between the first mean value and the fourth component, a third difference between the second mean value and the second component, a fourth difference between the second mean value and the fifth component, a fifth difference between the third mean value and the third component, and a sixth difference between the third mean value and the sixth component;

determining a covariance matrix using the first difference, the second difference, the third difference, the fourth difference, the fifth difference and the sixth difference;

determining a plurality of eigenvectors and a plurality of eigenvalues of the covariance matrix;

selecting a first, second and third eigenvalue based on the largest eigenvalues of the plurality of eigenvalues;

identifying first, second and third eigenvectors corresponding to the first, second and third eigenvalues; and

associating the first, second and third eigenvectors with a vertical acceleration component, a lateral acceleration component, or a longitudinal acceleration component.