Systems and methods for identifying distracted driving events using common features
A distracted driving analysis system for identifying distracted driving events is provided. The system includes a processor in communication with a memory device, and the processor is programmed to: (i) receive labeled training data, the labeled training data including driving event records (a) each labeled as an actual distracted driving event or a passenger event and (b) including phone usage by a user that occurred within a time period of a driving event, (ii) identify common features of the actual distracted driving events and the passenger events by processing the training data using a supervised machine learning algorithm, (iii) generate a trained model based at least in part upon the identified common features, (iv) process a new driving event, (v) assign the new driving event based at least in part upon features of the new driving event, and/or (vi) determine whether the new driving event is an actual distracted driving event or a passenger event.
1. A distracted driving analysis system for identifying distracted driving events, the distracted driving analysis system comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to:
receive labeled training data, the labeled training data including a plurality of driving event records, each driving event record of the plurality of driving event records being labeled as an actual distracted driving event or a passenger event, wherein each driving event record of the plurality of driving event records includes computing device usage by a user that occurred within a time period of a driving event associated with the user, the plurality of driving event records including a plurality of actual distracted driving events and a plurality of passenger events;
identify, using a supervised machine learning algorithm, one or more first common features of the plurality of actual distracted driving events by processing the labeled training data, the one or more first identified common features being related to driving or computing device usage, wherein at least one first identified common feature of the one or more first identified common features of the plurality of actual distracted driving events indicates how a user interacts with a user computing device of the user as a driver;
identify, using the supervised machine learning algorithm, one or more second common features of the plurality of passenger events by processing the labeled training data, the one or more second identified common features being related to driving or computing device usage, wherein at least one second identified common feature of the one or more second identified common features of the plurality of passenger events indicates how a user interacts with a user computing device of the user as a passenger;
generate a trained artificial neural network based at least in part upon the one or more first identified common features, the one or more second identified common features, and the labeled training data;
receive a new driving event associated with a second user; and
determine, using the trained artificial neural network, whether the new driving event is an actual distracted driving event or a passenger event.
2. The system of claim 1 , wherein the at least one processor is further programmed to:
assign a category to the new driving event as an actual distracted driving event or a passenger event.
3. The system of claim 2 , wherein the at least one processor is further programmed to assign a confidence level to the category assigned to the new driving event.
4. The system of claim 3 , wherein the at least one processor is further programmed to generate a driver profile for the second user, wherein the driver profile includes the categorized new driving event.
5. The system of claim 4 , wherein the at least one processor is further programmed to:
calculate a user safety score based at least in part upon the categorized new driving event; and
include the user safety score in the driver profile.
6. The system of claim 5 , wherein the at least one processor is further programmed to generate an insurance policy based at least in part upon the driver profile.
7. The system of claim 1 , wherein the one or more first common features include one of average tap rate, tap to swipe ratio, and computing device usage correlated to acceleration events.
8. A computer-implemented method for identifying distracted driving events using a distracted driving analysis system including at least one processor in communication with at least one memory device, the method comprising:
receiving labeled training data, the labeled training data including a plurality of driving event records, each driving event record of the plurality of driving event records being labeled as an actual distracted driving event or a passenger event, wherein each driving event record of the plurality of driving event records includes computing device usage by a user that occurred within a time period of a driving event associated with the user, the plurality of driving event records including a plurality of actual distracted driving events and a plurality of passenger events;
identifying, using a supervised machine learning algorithm, one or more first common features of the plurality of actual distracted driving events by processing the labeled training data, the one or more first identified common features being related to driving or computing device usage, wherein at least one first identified common feature of the one or more first identified common features of the plurality of actual distracted driving events indicates how a user interacts with a user computing device of the user as a driver;
identifying, using the supervised machine learning algorithm, one or more second common features of the plurality of passenger events by processing the labeled training data, the one or more second identified common features being related to driving or computing device usage, wherein at least one second identified common feature of the one or more second identified common features of the plurality of passenger events indicates how a user interacts with a user computing device of the user as a passenger;
generating a trained artificial neural network based at least in part upon the one or more first identified common features, the one or more second identified common features, and the labeled training data;
receiving a new driving event associated with a second user; and
determining, using the trained artificial neural network, whether the new driving event is an actual distracted driving event or a passenger event.
9. The computer-implemented method of claim 8 further comprising:
assigning a category to the new driving event as an actual distracted driving event or a passenger event.
10. The computer-implemented method of claim 9 further comprising assigning a confidence level to the category assigned to the new driving event.
11. The computer-implemented method of claim 10 further comprising generating a driver profile for the second user, wherein the driver profile includes the categorized new driving event.
12. The computer-implemented method of claim 11 further comprising:
calculating a user safety score based at least in part upon the categorized new driving event; and
including the user safety score in the driver profile.
13. The computer-implemented method of claim 12 further comprising generating an insurance policy based at least in part upon the driver profile.
14. The computer-implemented method of claim 8 , wherein the one or more first common features include one of average tap rate, tap to swipe ratio, and computing device usage correlated to acceleration events.
15. A non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by a distracted driving analysis system including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:
receive labeled training data, the labeled training data including a plurality of driving event records, each driving event record of the plurality of driving event records being labeled as an actual distracted driving event or a passenger event, wherein each driving event record of the plurality of driving event records includes computing device usage by a user that occurred within a time period of a driving event associated with the user, the plurality of driving event records including a plurality of actual distracted driving events and a plurality of passenger events;
identify, using the supervised machine learning algorithm, one or more first common features of the plurality of actual distracted driving events by processing the labeled training data, the one or more first identified common features being related to driving or computing device usage, wherein at least one first identified common feature of the one or more first identified common features of the plurality of actual distracted driving events indicates how a user interacts with a user computing device of the user as a drive;
identify, using the supervised machine learning algorithm, one or more second common features of the plurality of passenger events by processing the labeled training data, the one or more second identified common features being related to driving or computing device usage, wherein at least one second identified common feature of the one or more second identified common features of the plurality of passenger events indicates how a user interacts with a user computing device of the user as a passenger; and
generate a trained artificial neural network based at least in part upon the one or more first identified common features, the one or more second identified common features, and the labeled training data;
receive a new driving event associated with a second user; and
determine, using the trained artificial neural network, whether the new driving event is an actual distracted driving event or a passenger event.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the computer-executable instructions further cause the at least one processor to:
assign a category to the new driving event as an actual distracted driving event or a passenger event.
17. The non-transitory computer-readable storage medium of claim 16 , wherein the computer-executable instructions further cause the at least one processor to assign a confidence level to the category assigned to the new driving event.
18. The non-transitory computer-readable storage medium of claim 17 , wherein the computer-executable instructions further cause the at least one processor to generate a driver profile for the second user, wherein the driver profile includes the categorized new driving event.
19. The non-transitory computer-readable storage medium of claim 18 , wherein the computer-executable instructions further cause the at least one processor to:
calculate a user safety score based at least in part upon the categorized new driving event; and
include the user safety score in the driver profile.
20. The non-transitory computer-readable storage medium of claim 19 , wherein the computer-executable instructions further cause the at least one processor to generate an insurance policy based at least in part upon the driver profile.
21. The system of claim 1 , wherein the at least one processor is further programmed to:
assign a category to the new driving event by applying the trained artificial neural network to new driving data and new computing device usage data, the assigned category indicating whether the new driving event is an actual distracted driving event or a passenger event.