Facial recognition technology for improving motor carrier regulatory compliance
Methods for improving compliance with regulations pertaining to vehicle driving records are disclosed. One or more digital images from a camera mounted in a vehicle are received. Based on a determination that the vehicle has hours of service that have not been assigned to a driver, a subset of the one or more digital images corresponding to the hours of service are identified based on the timestamps. The subset of the one or more digital images are processed to identify a correspondence between a face of a person included in the one or more digital images and a face of a known person. Based on the correspondence transgressing a threshold level of correspondence, a user interface is generated for presentation on a device. The user interface includes an interactive user interface element for accepting a recommendation to assign the known person as the driver for the unassigned hours of service.
1. A system comprising:
one or more computer processors;
one or more computer memories; and
a set of instructions incorporated into the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations comprising:
capturing a set of images of faces of one or more persons in a vehicle during an occurrence of one or more predefined events;
based on a determination that the vehicle has hours of service that have been assigned to no driver of a plurality of drivers during the occurrence of the one or more predefined events, identifying a candidate driver from the plurality of drivers based on an image of the face of the candidate driver having a degree of similarity to one or more images of the set of images, the degree of similarity transgressing a threshold level; and
generating a user interface for presentation on a client device, the user interface including an interactive user interface element for accepting or rejecting a recommendation to assign the candidate driver as a driver of the vehicle during the occurrence of the one or more predefined events.
2. The system of claim 1 , wherein the one or more images were captured while a known person was operating the vehicle or an additional vehicle.
3. The system of claim 2 , the operations further comprising, based on the accepting or rejecting of the recommendation, adding the image of the face of the candidate driver to the set of images.
4. The system of claim 1 , the operations further comprising generating a training data set based on the accepting or rejecting of the recommendation, the training data set to be used to generate a machine-learned model usable for an additional identifying of the candidate driver.
5. The system of claim 1 , the operations further comprising, based on the accepting or rejection of the recommendation, generating an additional user interface, the additional user interface including an interactive user interface element for accepting or rejecting an additional recommendation to assign the candidate driver OF an additional vehicle having hours of service that have been assigned to no driver of the plurality of drivers.
6. The system of claim 1 , the operations further comprising generating an additional user interface for presentation on a client device, the additional user interface including one or more user interface elements for selecting the one or more predefined events from a plurality of predefined events, the plurality of predefined events including a starting of movement of the vehicle after a length of time of resting of the vehicle.
7. The system of claim 1 , the operations further comprising based on a determination that the vehicle has additional hours of service that have been assigned to no driver, automatically assigning the driver to the additional hours of service, the automatically assigning based on an application of a machine-learned model to the candidate driver and the one or more images.
8. A method comprising:
capturing a set of images of faces of one or more persons in a vehicle during an occurrence of one or more predefined events;
based on a determination that the vehicle has hours of service that have been assigned to no driver of a plurality of drivers during the occurrence of the one or more predefined events, identifying a candidate driver from the plurality of drivers based on an image of the face of the candidate driver having a degree of similarity to one or more images of the set of images, the degree of similarity transgressing a threshold level; and
generating a user interface for presentation on a client device, the user interface including an interactive user interface element for accepting or rejecting a recommendation to assign the candidate driver as a driver of the vehicle during the occurrence of the one or more predefined events.
9. The method of claim 8 , wherein the one or more images were captured while a known person was operating the vehicle or an additional vehicle.
10. The method of claim 9 , further comprising, based on the accepting or rejecting of the recommendation, adding the image of the face of the candidate driver to the set of images.
11. The method of claim 8 , further comprising generating a training data set based on the accepting or rejecting of the recommendation, the training data set to be used to generate a machine-learned model usable for an additional identifying of the candidate driver.
12. The method of claim 8 , further comprising, based on the accepting or rejection of the recommendation, generating an additional user interface, the additional user interface including an interactive user interface element for accepting or rejecting an additional recommendation to assign the candidate driver OF an additional vehicle having hours of service that have been assigned to no driver of the plurality of drivers.
13. The method of claim 8 , further comprising generating an additional user interface for presentation on a client device, the additional user interface including one or more user interface elements for selecting the one or more predefined events from a plurality of predefined events, the plurality of predefined events including a starting of movement of the vehicle after a length of time of resting of the vehicle.
14. The method of claim 8 , further comprising based on a determination that the vehicle has additional hours of service that have been assigned to no driver, automatically assigning the driver to the additional hours of service, the automatically assigning based on an application of a machine-learned model to the candidate driver and the one or more images.
15. A non-transitory computer-readable storage medium storing a set of instructions that, when executed by one or more computer processors, causes the one or more computer processors to perform operations, the operations comprising:
capturing a set of images of faces of one or more persons in a vehicle during an occurrence of one or more predefined events;
based on a determination that the vehicle has hours of service that have been assigned to no driver of a plurality of drivers during the occurrence of the one or more predefined events, identifying a candidate driver from the plurality of drivers based on an image of the face of the candidate driver having a degree of similarity to one or more images of the set of images, the degree of similarity transgressing a threshold level; and
generating a user interface for presentation on a client device, the user interface including an interactive user interface element for accepting or rejecting a recommendation to assign the candidate driver as a driver of the vehicle during the occurrence of the one or more predefined events.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the one or more images were captured while a known person was operating the vehicle or an additional vehicle.
17. The non-transitory computer-readable storage medium of claim 16 , the operations further comprising, based on the accepting or rejecting of the recommendation, adding the image of the face of the candidate driver to the set of images.
18. The non-transitory computer-readable storage medium of claim 15 , the operations further comprising generating a training data set based on the accepting or rejecting of the recommendation, the training data set to be used to generate a machine-learned model usable for an additional identifying of the candidate driver.
19. The non-transitory computer-readable storage medium of claim 15 , the operations further comprising, based on the accepting or rejection of the recommendation, generating an additional user interface, the additional user interface including an interactive user interface element for accepting or rejecting an additional recommendation to assign the candidate driver OF an additional vehicle having hours of service that have been assigned to no driver of the plurality of drivers.
20. The non-transitory computer-readable storage medium of claim 15 , the operations further comprising generating an additional user interface for presentation on a client device, the additional user interface including one or more user interface elements for selecting the one or more predefined events from a plurality of predefined events, the plurality of predefined events including a starting of movement of the vehicle after a length of time of resting of the vehicle.