Driver Log Analytics System

• US 20170017927A1
• Filed: 07/14/2015
• Published: 01/19/2017
• Est. Priority Date: 07/14/2015
• Status: Active Grant

• Current Assignee: Omnitracs LLC (Solera Holdings Incorporated)
• Original Assignee: Omnitracs LLC (Solera Holdings Incorporated)
• Inventors: DOMNICK, Lauren, CROKE, Dean, TAN, Budi, OLD, Andrew, RITTO, Patrick, GILCREASE, Jeffrey
• Granted Patent: US 10,504,068 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G06Q 10/1091 Recording time for administrative or management purposes

Abstract

Aspects of notification of a predicted driving event include receiving driver log information associated with one or more drivers respectively corresponding to one or more vehicles, and aggregating the driver log information for a predetermined time period. Further, the aspects include extracting derivations from the aggregated driver log information in accordance with one or more derivation rules associated with an event prediction model operable to predict an adverse driving event. Also, the aspects include applying the event prediction model to the derivations to generate an adverse driver event prediction for each of the one or more drivers. Additionally, the aspects include transmitting a notification message including one or more of the adverse driver event predictions.

Claims

1. A method of notification of a predicted driving event, comprising:

receiving driver log information associated with one or more drivers respectively corresponding to one or more vehicles;

aggregating the driver log information for a predetermined time period;

extracting derivations from the aggregated driver log information in accordance with one or more derivation rules associated with an event prediction model operable to predict an adverse driving event;

applying the event prediction model to the derivations to generate an adverse driver event prediction for each of the one or more drivers; and

transmitting a notification message including one or more of the adverse driver event predictions.

2. The method of claim 1, wherein the driver log information includes one or more log codes indicating one or more of:

whether one of the one or more drivers is driving at a sampled time point,

whether the driver is on duty but not driving at the sampled time point,

whether the driver is resting at the sampled time point, and

whether the driver is off duty at the sampled time point.

3. The method of claim 1, wherein the derivations include:

a first number of hours that one of the one or more drivers rests in a first time period during a week,

a second number of times that the driver violates rules or regulations,

a third number of times that the driver had sufficient hours of rest during the week, and

a fourth number of times that the driver did not have at least ten hours off duty in any day in the week.

4. The method of claim 1, wherein the adverse driver event includes one of a preventable accident, or a severe accident, or a traffic rule violation, or an hours-of-service violation.

5. The method of claim 1, further comprising ranking the one or more drivers in accordance with a value of the confidence factor.

6. The method of claim 1, further comprising triggering a remedial measure for at least one of the one or more drivers based on a value of the confidence factor.

7. The method of claim 1, wherein extracting the derivations further comprises extracting a subset of at least one of the driver log information and aggregated driver log information that indicate that a respective driver is in danger of violating a government-based or fleet operator-based traffic rule.

8. The method of claim 1, wherein extracting the derivations further comprises extracting a subset of at least one of the driver log information and aggregated driver log information that indicate that a respective driver is in danger of violating a government-based or fleet operator-based hours-of-service regulation.

9. The method of claim 1, wherein each adverse driver event prediction includes a confidence factor that indicates a likelihood of whether each of the one or more drivers will experience the adverse driving event in a subsequent time period.

10. An apparatus for notification of a predicted driving event, comprising:

means for receiving driver log information associated with one or more drivers respectively corresponding to one or more vehicles;

means for aggregating the driver log information for a predetermined time period;

means for extracting derivations from the aggregated driver log information in accordance with one or more derivation rules associated with an event prediction model operable to predict an adverse driving event;

means for applying the event prediction model to the derivations to generate an adverse driver event prediction for each of the one or more drivers; and

means for transmitting a notification message including one or more of the adverse driver event predictions.

11. The apparatus of claim 10, wherein the driver log information includes one or more log codes indicating one or more of:

whether one of the one or more drivers is driving at a sampled time point,

whether the driver is on duty but not driving at the sampled time point,

whether the driver is resting at the sampled time point, and

whether the driver is off duty at the sampled time point.

12. The apparatus of claim 10, wherein the derivations include:

a first number of hours that one of the one or more drivers rests in a first time period during a week,

a second number of times that the driver violates rules or regulations,

a third number of times that the driver had sufficient hours of rest during the week, and

a fourth number of times that the driver did not have at least ten hours off duty in any day in the week.

13. The apparatus of claim 10, wherein the adverse driver event includes one of a preventable accident, or a severe accident, or a traffic rule violation, or an hours-of-service violation.

14. The apparatus of claim 10, further comprising means for ranking the one or more drivers in accordance with a value of the confidence factor.

15. The apparatus of claim 10, further comprising means for triggering a remedial measure for at least one of the one or more drivers based on a value of the confidence factor.

16. The apparatus of claim 10, wherein means for extracting the derivations further comprises means for extracting a subset of at least one of the driver log information and aggregated driver log information that indicate that a respective driver is in danger of violating a government-based or fleet operator-based traffic rule.

17. The apparatus of claim 10, wherein means for extracting the derivations further comprises means for extracting a subset of at least one of the driver log information and aggregated driver log information that indicate that a respective driver is in danger of violating a government-based or fleet operator-based hours-of-service regulation.

18. The apparatus of claim 10, wherein each adverse driver event prediction includes a confidence factor that indicates a likelihood of whether each of the one or more drivers will experience the adverse driving event in a subsequent time period.

19. A computer-readable medium storing code for notification of a predicted driving event, the code comprising instructions executable to:

receive driver log information associated with one or more drivers respectively corresponding to one or more vehicles;

aggregate the driver log information for a predetermined time period;

extract derivations from the aggregated driver log information in accordance with one or more derivation rules associated with an event prediction model operable to predict an adverse driving event;

apply the event prediction model to the derivations to generate an adverse driver event prediction for each of the one or more drivers; and

transmit a notification message including one or more of the adverse driver event predictions.

20. The computer-readable medium of claim 19, wherein the adverse driver event includes one of a preventable accident, or a severe accident, or a traffic rule violation, or an hours-of-service violation.