Systems and methods for implementing a tracking camera system onboard an autonomous vehicle

1. A computer-implemented method for implementing a tracking camera system onboard an autonomous vehicle comprising:

obtaining, by a computing system, point cloud data of an environment from a light detection and ranging (LiDAR) sensor of the autonomous vehicle;

determining, by the computing system, based on the point cloud data, that a confidence score of an object in the environment is below a threshold score, the confidence score indicating that the object is beyond a threshold identification distance of the LiDAR sensor;

in response to determining that the confidence score of the object is below the threshold score, receiving, by the computing system, coordinate data of the object from the LiDAR sensor, wherein the coordinate data is determined based on pulsed laser lights of the LiDAR sensor reflected off from the object and a rotation of the LiDAR sensor;

actuating, by the computing system, based on the coordinate data, the tracking camera system to a position such that the object is in view of the tracking camera system;

receiving, by the computing system, vehicle operation data of the autonomous vehicle;

adjusting, by the computing system, based on the vehicle operation data, the position of the tracking camera system such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion;

adjusting, by the computing system, a focus of the tracking camera system to bring the object in focus; and

capturing, by the computing system, image data corresponding to the object using the tracking camera system.

2. The computer-implemented method of claim 1 , further comprising:

analyzing the image data, based on one or more object identification and recognition techniques, to identify the object; and

determining a confidence score associated with the identified object.

3. The computer-implemented method of claim 1 , wherein adjusting the focus of the tracking camera system to bring the object in focus comprises:

determining a phase difference between the object and the tracking camera system; and

actuating, based on the phase difference, one or more lenses in an optical system of the tracking camera to bring the object in focus.

4. The computer-implemented method of claim 1 , wherein the vehicle operation data comprises at least a vehicle speed and a vehicle direction of the autonomous vehicle.

5. The computer-implemented method of claim 4 , wherein adjusting the position of the tracking camera system such that the object remains in view of the tracking camera system comprises:

rotating the tracking camera system, continuously, in an azimuth direction, to account for the vehicle speed and the vehicle direction; and

pivoting the tracking camera system, continuously, in an elevation direction, to account for the vehicle speed and the vehicle direction.

6. The computer-implemented method of claim 1 , wherein the coordinate data comprises azimuth data and elevation data corresponding to a location of the object.

7. The computer-implemented method of claim 6 , wherein the azimuth data corresponding to the location of the object is determined based on the rotation and point cloud data of the LiDAR sensor.

8. The computer-implemented method of claim 6 , wherein the elevation data corresponding to the location of the object is determined based on point cloud data of the LiDAR sensor.

9. The computer-implemented method of claim 6 , wherein actuating the tracking camera system to the position such that the object is in view of the tracking camera comprises:

rotating the tracking camera system to an azimuth position corresponding to the azimuth data; and

pivoting the tracking camera system to an elevation position corresponding to the elevation data.

10. A computing system for implementing a tracking camera system onboard an autonomous vehicle comprising:

one or more processors; and

a memory storing instructions that, when executed by the one or more processor, causes the computing system to perform:

obtaining point cloud data of an environment from a light detection and ranging (LiDAR) sensor of the autonomous vehicle;

determining, based on the point cloud data, that a confidence score of an object in the environment is below a threshold score, the confidence score indicating that the object is beyond a threshold identification distance of the LiDAR sensor;

receiving coordinate data of the object from the LiDAR sensor, wherein the coordinate data is determined based on pulsed laser lights of the LiDAR sensor reflected off from the object and a rotation of the LiDAR sensor;

actuating, based on the coordinate data, the tracking camera system to a position such that the object is in view of the tracking camera system;

receiving vehicle operation data of the autonomous vehicle;

adjusting, based on the vehicle operation data, the position of the tracking camera system such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion;

adjusting a focus of the tracking camera system to bring the object in focus; and

capturing image data corresponding to the object using the tracking camera system.

11. The computing system of claim 10 , wherein the memory storing instructions causes the system to further perform:

analyzing the image data, based on one or more object identification and recognition techniques, to identify the object; and

determining a confidence score associated with the identified object.

12. The computing system of claim 10 , wherein actuating the tracking camera system to the position such that the object is in view of the tracking camera comprises:

rotating the tracking camera system to an azimuth position corresponding to azimuth data of the coordinate data; and

pivoting the tracking camera system to an elevation position corresponding to elevation data of the coordinate data.

13. The computing system of claim 10 , wherein adjusting the position of the tracking camera system such that the object remains in view of the tracking camera system comprises:

rotating the tracking camera system, continuously, in an azimuth direction, to account for a vehicle speed and a vehicle direction of the autonomous vehicle; and

pivoting the tracking camera system, continuously, in an elevation direction, to account for the vehicle speed and the vehicle direction of the autonomous vehicle.

14. A non-transitory computer readable medium comprising instructions that, when executed, cause one or more processors of a tracking camera system onboard an autonomous vehicle to perform:

obtaining point cloud data of an environment from a light detection and ranging (LiDAR) sensor of the autonomous vehicle;

determining, based on the point cloud data, that a confidence score of an object in the environment is below a threshold score, the confidence score indicating that the object is beyond a threshold identification distance of the LiDAR sensor;

receiving coordinate data of an object, wherein the coordinate data is determined based on pulsed laser lights of a light detection and ranging (LiDAR) sensor reflected off from the object and a rotation of the LiDAR sensor;

actuating, based on the coordinate data, the tracking camera system to a position such that the object is in view of the tracking camera system;

receiving vehicle operation data of the autonomous vehicle;

adjusting, based on the vehicle operation data, the position of the tracking camera system such that the object remains in view of the tracking camera system while the autonomous vehicle is in motion;

adjusting a focus of the tracking camera system to bring the object in focus; and

capturing image data corresponding to the object using the tracking camera system.

15. The non-transitory computer readable medium of claim 14 , wherein the instructions, when executed, causes the one or more processors of the tracking camera system to further perform:

analyzing the image data, based on one or more object identification and recognition techniques, to identify the object; and

determining a confidence score associated with the identified object.

16. The non-transitory computer readable medium of claim 14 , wherein the coordinate data comprises azimuth data and elevation data corresponding to a location of the object.

17. The non-transitory computer readable medium of claim 16 , wherein the azimuth data corresponding to the location of the object is determined based on the rotation and point cloud data of the LiDAR sensor.

18. The non-transitory computer readable medium of claim 16 , wherein actuating the tracking camera system to the position such that the object is in view of the tracking camera comprises:

rotating the tracking camera system to an azimuth position corresponding to the azimuth data; and

pivoting the tracking camera system to an elevation position corresponding to the elevation data.

19. The non-transitory computer readable medium of claim 14 , wherein the vehicle operation data comprises at least a vehicle speed and a vehicle direction of the autonomous vehicle.

20. The non-transitory computer readable medium of claim 19 , wherein adjusting the position of the tracking camera system such that the object remains in view of the tracking camera system comprises:

rotating the tracking camera system, continuously, in an azimuth direction, to account for the vehicle speed and the vehicle direction; and

pivoting the tracking camera system, continuously, in an elevation direction, to account for the vehicle speed and the vehicle direction.