Identifying suspicious entities using autonomous vehicles
Systems and methods for identifying suspicious entities using autonomous vehicles are disclosed. In one embodiment, a method is disclosed comprising identifying a suspect vehicle using at least one digital camera equipped on an autonomous vehicle; identifying a set of candidate autonomous vehicles; enabling, on each of the candidate autonomous vehicles, a search routine, the search routine instructing each respective autonomous vehicle to coordinate tracking of the suspect vehicle; recording, while tracking the suspect vehicle, a plurality of images of the suspect vehicle; periodically re-calibrating the search routines executed by the autonomous vehicles based on the plurality of images; and re-routing the autonomous vehicles based on the re-calibrated search routines.
1. A method comprising:
receiving an identification of a vehicle;
identifying an autonomous vehicle near the vehicle;
recording, by the autonomous vehicle, an image of the vehicle; and
re-routing the autonomous vehicle.
2. The method of claim 1 , wherein re-routing the autonomous vehicle comprises terminating routing of the autonomous vehicle when an image recorded by the autonomous vehicle does not include the vehicle.
3. The method of claim 1 , wherein re-routing the autonomous vehicle comprises transmitting updated routing information to the autonomous vehicle.
4. The method of claim 1 , wherein receiving an identification of a vehicle comprises receiving an image captured by a digital camera installed in the autonomous vehicle and analyzing the image using a deep neural network.
5. The method of claim 4 , wherein the deep neural network is trained using a video game simulation.
6. The method of claim 1 , further comprising:
identifying a new image that includes the vehicle;
determining a location associated with an autonomous vehicle that captured the new image; and
generating updated routing information based on the location.
7. The method of claim 1 , wherein re-routing the autonomous vehicle further comprises predicting a future route of the vehicle and re-routing the autonomous vehicle based on the future route.
8. A device comprising:
a processor; and
a storage medium for tangibly storing thereon program logic for execution by the processor, the stored program logic comprising logic for:
receiving an identification of a vehicle,
identifying an autonomous vehicle near the vehicle,
recording, by the autonomous vehicle, an image of the vehicle, and
re-routing the autonomous vehicle.
9. The device of claim 8 , wherein re-routing the autonomous vehicle comprises terminating routing of the autonomous vehicle if an image recorded by the autonomous vehicle does not include the vehicle.
10. The device of claim 8 , wherein re-routing the autonomous vehicle comprises transmitting updated routing information to the autonomous vehicle.
11. The device of claim 8 , wherein receiving an identification of a vehicle comprises receiving an image captured by a digital camera installed in the autonomous vehicle and analyzing the image using a deep neural network.
12. The device of claim 11 , wherein the deep neural network is trained using a video game simulation.
13. The device of claim 8 , the stored program logic further comprising logic for:
identifying a new image that includes the vehicle;
determining a location associated with an autonomous vehicle that captured the new image; and
generating updated routing information based on the location.
14. The device of claim 8 , wherein re-routing the autonomous vehicle further comprises predicting a future route of the vehicle and re-routing the autonomous vehicle based on the future route.
15. A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
receiving an identification of a vehicle;
identifying an autonomous vehicle near the vehicle;
recording, by the autonomous vehicle, an image of the vehicle; and
re-routing the autonomous vehicle.
16. The non-transitory computer-readable storage medium of claim 15 , wherein re-routing the autonomous vehicle comprises terminating routing of the autonomous vehicle if an image recorded by the autonomous vehicle does not include the vehicle.
17. The non-transitory computer-readable storage medium of claim 15 , wherein re-routing the autonomous vehicle comprises transmitting updated routing information to the autonomous vehicle.
18. The non-transitory computer-readable storage medium of claim 15 , wherein receiving an identification of a vehicle comprises receiving an image captured by a digital camera installed in the autonomous vehicle and analyzing the image using a deep neural network.
19. The non-transitory computer-readable storage medium of claim 15 , wherein the computer program instructions further define the steps of:
identifying a new image that includes the vehicle;
determining a location associated with an autonomous vehicle that captured the new image; and
generating updated routing information based on the location.
20. The non-transitory computer-readable storage medium of claim 15 , wherein re-routing the autonomous vehicle further comprises predicting a future route of the vehicle and re-routing the autonomous vehicle based on the future route.