PREDICTING DRIVER BEHAVIOR BASED ON USER DATA AND VEHICLE DATA

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.