PREDICTING DRIVER BEHAVIOR BASED ON USER DATA AND VEHICLE DATA

• US 20150213555A1
• Filed: 01/27/2014
• Published: 07/30/2015
• Est. Priority Date: 01/27/2014
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: HTI IP LLC (Verizon Communications Inc.)
• Inventors: BARFIELD, James Ronald Jr., WELCH, Stephen Christopher
• Application Number:
  US14/164,862
• Publication Number:
  US 20150213555A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/48 for in-vehicle communication

Abstract

A system may determine driving information associated with a group of users, the driving information may be based on sensor information collected by at least two of a group of user devices, a first group of vehicle devices connected to a corresponding group of vehicles associated with the group of users, or a group of second vehicle devices installed in the corresponding group of vehicles. The system may determine non-driving information associated with the group of users. The system may create a driver behavior prediction model based on the driving information and the non-driving information, and may store the driver behavior prediction model. The driver behavior prediction model may permit a driver prediction to be made regarding a particular user (e.g., a user that is not necessarily included in the group of users). The driver behavior prediction may be associated with a particular geographic location.

Claims

1. A system, comprising:

one or more devices to;

determine driving information associated with a group of users,

the driving information being based on sensor information collected by at least two of a group of user devices, a first group of vehicle devices used in association with a corresponding group of vehicles associated with the group of users, or a group of second vehicle devices installed in the corresponding group of vehicles;

determine non-driving information associated with the group of users;

create a driver behavior prediction model based on the driving information, and the non-driving information; and

store the driver behavior prediction model,

the driver behavior prediction model permitting a driver prediction to be made regarding a particular user.

2. The system of claim 1, where the driving information includes:

distraction information associated with a user of the group of users,

when determining the distraction information, the one or more devices are to;

collect sensor information associated with a vehicle,

the vehicle being associated with the user;

determine, based on the sensor information, that the vehicle is in motion;

determine that the user, associated with the vehicle, is interacting with a user device while the vehicle is in motion; and

determine the distraction information based on determining that the user is interacting with the user device while the vehicle is in motion.

3. The system of claim 1, where the driving information includes:

suspicious behavior information associated with a user of the group of users,

when determining the suspicious behavior information, the one or more devices are to;

collect sensor information associated with a user device,

the user device being associated with the user;

determine, based on the sensor information, that the user device has been powered off for a threshold amount of time;

determine that a vehicle, associated with the user, has been driven while the user device was powered off; and

determine the suspicious behavior information based on determining that the vehicle was driven while the user device was powered off.

4. The system of claim 1, where the driving information includes:

accident information associated with a user of the group of users,

when determining the accident information, the one or more devices are to;

collect sensor information associated with a vehicle,

the vehicle being associated with the user;

determine, based on the sensor information, information indicating that an acceleration event, associated with the vehicle, has occurred;

determine that a vehicle accident, involving the vehicle, has occurred based on the information indicating that the acceleration event has occurred and the sensor information; and

determine the accident information based on determining that the vehicle accident has occurred.

5. The system of claim 1, where the driving information includes:

distance information associated with a particular acceleration event and a user of the group of users,

when determining the distance information, the one or more devices are to;

determine acceleration event information associated with a group of acceleration events,

the group of acceleration events being associated with the group of users;

determine distance information for the particular acceleration event based on the acceleration event information associated with the group of acceleration events.

6. The system of claim 1, where the one or more devices are further to:

determine that the driver prediction, associated with the particular user, is to be generated using the driver behavior prediction model;

determine driving information associated with the particular user,

the driving information associated with the particular user being based on sensor information collected by a user device associated with the particular user,

the driving information associated with the particular user being based on sensor information collected by a first vehicle device associated with the particular user,

the first vehicle device being connected to a vehicle associated with the particular user, or

the driving information associated with the particular user being based sensor information collected by a second vehicle device associated with the particular user,

the second vehicle device being installed in the vehicle associated with the particular user;

determine non-driving information associated with the particular user;

generate the driver prediction by inputting the driving information associated with the particular user and the non-driving information associated with the particular user into the driver behavior prediction model; and

provide the driver prediction for display.

7. The system of claim 1, where the driver prediction includes at least one of:

a driver score associated with the particular driver;

a percentage of likelihood associated with the particular driver;

or

a driver score bias associated with the particular driver.

8. A system, comprising:

one or more devices;

receive sensor information collected by a set of collection devices,

the set of collection devices including one or more user devices and one or more vehicle devices;

determine driving information associated with a set of users,

the set of users corresponding to the set of collection devices,

the driving information being based on the sensor information, and including information that identifies a geographic location associated with the set of users;

determine non-driving information associated with the set of users and the geographic location;

create a driver behavior prediction model based on the driving information and the non-driving information; and

store the driver behavior prediction model,

the driver behavior prediction model permitting a driver prediction to be made regarding a particular user.

9. The system of claim 8, where the set of collection devices include at least one of:

a smart phone;

an onboard diagnostics device associated with a vehicle;

or

a telematics device associated with a vehicle.

10. The system of claim 8, where the set of collection devices include:

a telematics device that interfaces with a communication bus of a vehicle.

11. The system of claim 8, where the driving information includes:

accident information associated with a user of the set of users,

when determining the accident information, the one or more devices are to;

collect sensor information associated with a vehicle,

the vehicle being associated with the user;

determine, based on the sensor information, information indicating that an acceleration event, associated with the vehicle, has occurred;

determine that a vehicle accident, involving the vehicle, has occurred based on the information indicating that the acceleration event has occurred and the sensor information; and

determine the accident information based on determining that the vehicle accident has occurred.

12. The system of claim 8, where the driving information includes:

distance information associated with a particular acceleration event and a user of the set of users,

when determining the distance information, the one or more devices are to;

determine acceleration event information associated with a group of acceleration events,

the group of acceleration events being associated with the set of users;

determine distance information for the particular acceleration event based on the acceleration event information associated with the group of acceleration events.

13. The system of claim 8, where the one or more devices are further to:

determine that a driver behavior prediction, associated with a particular user, is to be generated using the driver behavior prediction model;

determine driving information associated with the particular user,

the driving information associated with the particular user being based on sensor information collected by a collection device associated with the particular user;

generate the driver behavior prediction by inputting the driving information associated with the particular user and the non-driving information associated with the particular user into the driver behavior prediction model; and

present, for display, the driver behavior prediction.

14. The system of claim 13, where the driver behavior prediction includes at least one of:

a driver score associated with the particular user;

a percentage of likelihood associated with the particular user;

or

a driver score bias associated with the particular user.

15. A method, comprising:

determining, by one or more devices, driving information associated with a plurality of users and a particular geographic location,

the driving information being based on sensor information collected by user devices and/or vehicle devices, associated with the plurality of users, at the particular geographic location;

determining, by the one or more devices, non-driving information associated with the plurality of users and/or the particular geographic location;

creating, by the one or more devices, a driver behavior prediction model based on the driving information and the non-driving information; and

storing, by the one or more devices, the driver behavior prediction model,

the driver behavior prediction model associating driving information, associated with the plurality of users, and non-driving information, associated with the plurality of users, and/or the particular geographic location, and

the driver behavior prediction model allowing a driver prediction, associated with a particular user and the particular geographic location, to be generated.

16. The method of claim 15, further comprising:

determining additional driving information associated with the particular user, and additional non-driving information associated with the particular user; and

biasing the driver prediction, associated with the particular user, based on the driver behavior prediction model, the additional driving information, and the additional non-driving information.

17. The method of claim 15, further comprising:

determining first acceleration event information associated with the particular user and the particular geographic location,

the first acceleration event information being of an event type associated with a vehicle stop at the particular geographic location, an event type associated with a vehicle start event at the particular geographic location, or an event type associated with a vehicle turn event at the particular geographic location;

determining second acceleration event information associated with the plurality of users and the particular geographic location,

the second acceleration event being of a same event type as the event type of the first acceleration event;

comparing the first acceleration event information and the second acceleration event information; and

biasing the driver prediction, associated with the particular user, based on the comparing the first acceleration event information and the second acceleration event information.

18. The method of claim 15, where the non-driving information includes at least one of:

driver demographic information;

a driver age;

information associated with a marital status;

driver health information;

biometric authentication information;

a time of day;

information associated with a quantity of light;

information associated with social networking activity;

information associated with phone usage;

information associated with text messaging;

traffic information;

or

weather information.

19. The method of claim 15, where the driving information includes:

distraction information associated with the particular geographic location and a user of the plurality of users;

the distraction information being determined by;

collecting sensor information associated with a vehicle,

the vehicle being associated with the user;

determining, based on the sensor information, that the vehicle is in motion;

determining that the user, associated with the vehicle, is interacting with a user device while the vehicle is in motion; and

determining the distraction information based on determining that the user is interacting with the user device while the vehicle is in motion.

20. The method of claim 15, further comprising:

determining that the driver prediction, associated with the particular user, is to be generated using the driver behavior prediction model;

determining driving information associated with the particular user and the particular geographic location,

the driving information being based on sensor information collected by a user device and/or a vehicle device associated with the particular user;

determining non-driving information associated with the particular user and the particular geographic location;

generating the driver prediction by inputting the driving information, associated with the particular user and the particular geographic location, and the non-driving information, associated with the particular user and/or the particular geographic location, into the driver behavior prediction model; and

providing, for display, the driver prediction.