System and method for determining driving patterns using telematics data

• US 10,319,159 B1
• Filed: 05/16/2018
• Issued: 06/11/2019
• Est. Priority Date: 05/15/2014
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Menon, Sunish Shreenarayan, Dosher, David J., Christensen, Scott Thomas
• Primary Examiner(s): Borlinghaus, Jason
• Assistant Examiner(s): Alladin, Ambreen A.
• Application Number:
  US15/980,892
• Time in Patent Office: 391 Days
• Field of Search: 705 37, 705 44
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0825 using optical means
• CPC Class Codes: G07C 5/085 using electronic data carriers

Abstract

A computer implemented method for determining a driving pattern from raw telematics data is presented. A data server may receive, via a computer network, a plurality of telematics data corresponding to a trip of a vehicle, wherein the plurality of telematics data originates from a client computing device. The data server may also identify a first primary movement window of the vehicle trip and one or more constant speed and idling windows of the vehicle trip. The data server may further estimate gravity from the telematics data in the first primary movement window of the vehicle trip and generate a pitch and a roll angle from the first primary movement window of the vehicle trip, as well as one or more yaw angle estimates from the first primary movement window of the vehicle trip. The data server may further determine a driving pattern using at least constant speed times, idling times, acceleration, breaking, vehicle turns and relate that to estimate driving risk and insurance premium.

Claims

1. A computer-implemented method for determining a driving pattern from raw telematics data originated from a client computing device in a vehicle, the method comprising:

removing, by one or more processors, a gravitational data component from telematics data in a first primary movement window of a vehicle trip leaving a vector defined by a longitudinal data component and a latitudinal data component in a longitudinal-latitudinal plane, wherein the first primary movement window is indicative of the client computing device being static with respect to movement of the vehicle;

combining, by the one or more processors, the longitudinal data component and the latitudinal data component to create an angular average;

generating, by the one or more processors, one or more yaw angle estimates from the primary movement window of the vehicle trip using the angular average; and

determining, by the one or more processors, a driving pattern using at least one of the yaw angle estimates.

2. The method of claim 1 further comprising:

determining, by the one or more processors, a driving pattern using at least one of;

a constant speed time, an idling time, an acceleration event, a braking event, a turning event, GPS data, gyroscope data and magnetometer data.

3. The method of claim 1 further comprising:

summarizing, by the one or more processors, the driving pattern from the first primary movement windows at one or more of;

a trip, a day, a month or a year level.

4. The method of claim 1 further comprising:

receiving, via a computer network, a plurality of GPS data corresponding to a trip of the vehicle, wherein the telematics data originates from the client computing device and includes a GPS speed data;

analyzing, by the one or more processors, the plurality of GPS data; and

determining, by the one or more processors, that the plurality of GPS data is accurate.

5. The method of claim 1 further comprising:

generating, by the one or more processors, a pitch and a roll angle from the first primary movement window of the vehicle trip;

determining, by the one or more processors, a driving pattern using at least one of;

the pitch and roll angles; and

determining, by the one or more processors, that using at least the pitch and roll angles is indicative of at least one of;

an acceleration data of an acceleration event, a braking event, a left turn event or a right turn event.

6. The method of claim 1 further comprising:

analyzing, by the one or more processors, the one or more yaw angle estimates; and

determining, by the one or more processors, a final yaw angle from the one or more yaw angle estimates that best aligns the acceleration data with the plurality of telematics data.

7. A computer device for determining a driving pattern from raw telematics data originated from a client computing device in a vehicle, the computer device comprising:

one or more processors; and

one or more memories coupled to the one or more processors;

the one or more memories including non-transitory computer executable instructions stored therein that, when executed by the one or more processors, cause the one or more processors to;

remove a gravitational data component from telematics data in a first primary movement window of a vehicle trip leaving a vector defined by a longitudinal data component and a latitudinal data component in a longitudinal-latitudinal plane, wherein the first primary movement window is indicative of the client computing device being static with respect to movement of the vehicle;

combine the longitudinal data component and the latitudinal data component to create an angular average;

generate, using the angular average, one or more yaw angle estimates from the first primary movement window of the vehicle trip; and

determine a driving pattern based upon at least one of the yaw angle estimates.

8. The computer device of claim 7, wherein the non-transitory computer executable instructions further cause the one or more processors to:

summarize the driving pattern from the first primary movement windows at one or more of;

a trip, a day, a month and a year level and relating that with an insurance account to determine driving risk.

9. The computer device of claim 7, wherein the non-transitory computer executable instructions further cause the one or more processors to:

receive a plurality of GPS data corresponding to a trip of the vehicle, wherein the telematics data originates from the client computing device and includes a GPS speed data;

analyze the plurality of GPS data; and

determine that the plurality of GPS data is accurate.

10. The computer device of claim 7, wherein the non-transitory computer executable instructions further cause the one or more processors to:

analyze the one or more yaw angle estimates; and

determine a final yaw angle from the one or more yaw angle estimates that best aligns the acceleration data with the telematics data.

11. The computer device of claim 7, wherein the non-transitory computer executable instructions further cause the one or more processors to:

generate a pitch and a roll angle from the first primary movement window of the vehicle trip;

determine a driving pattern using at least one of;

the pitch and roll angles; and

determine that using at least the pitch and roll angles is indicative of at least one of;

an acceleration of an acceleration event, a braking event, a left turn event or a right turn event.

12. A non-transitory computer readable storage medium having instructions stored thereon for determining a driving pattern from raw telematics data originated from a client computing device in a vehicle, the instructions when executed on one or more processors cause the one or more processors to:

remove a gravitational data component from the telematics data in a first primary movement window of a vehicle trip leaving a vector defined by a longitudinal data component and a latitudinal data component in a longitudinal-latitudinal plane, wherein the first primary movement window is indicative of the client computing device being static with respect to movement of the vehicle;

combine the longitudinal data component and the latitudinal data component to create an angular average of four times the directions of all vectors in the longitudinal-latitudinal plane such that all four acceleration effects of the plurality of telematics data are in one direction;

generate, using the angular average, one or more yaw angle estimates from the first primary movement window of the vehicle trip; and

determine a driving pattern based upon at least one of the yaw angle estimates.

13. The non-transitory computer readable storage medium of claim 12, further comprising instructions stored thereon that cause the one or more processors to:

summarize the driving pattern from the first primary movement windows at one or more of;

a trip, a day, a month and a year level.

14. The non-transitory computer readable storage medium of claim 12, further comprising instructions stored thereon that cause the one or more processors to:

receive a plurality of GPS data corresponding to a trip of the vehicle, wherein the telematics data originates from the client computing device and includes a GPS speed data;

analyze the plurality of GPS data; and

determine that the plurality of GPS data is accurate.

15. The non-transitory computer readable storage medium of claim 12, further comprising instructions stored thereon that cause the one or more processors to:

analyze the one or more yaw angle estimates; and

determine a final yaw angle from the one or more yaw angle estimates that best aligns the acceleration data with the telematics data.

16. The non-transitory computer readable storage medium of claim 12, further comprising instructions stored thereon that cause the one or more processors to:

generate a pitch and a roll angle from the first primary movement window of the vehicle trip;

determine a driving pattern using at least one of;

the pitch and roll angles; and

determine that using at least the pitch and roll angles is indicative of at least one of;

an acceleration of an acceleration event, a braking event, a left turn event or a right turn event.

17. The non-transitory computer readable storage medium of claim 12, further comprising instructions stored thereon that cause the one or more processors to:

determine a driving pattern using at least one of;

a constant speed time, an idling time, an acceleration event, a braking event, a turning event GPS data, gyroscope data and magnetometer data.