System and method for adaptive cruise control for low speed following

1. A system comprising:

a data processor; and

an adaptive cruise control module, executable by the data processor, being configured to:

receive input object data from a subsystem of an autonomous vehicle, the input object data including image data from a video stream generated by an image generating device, the input object data also including distance data and velocity data relative to a lead vehicle;

process the image data by semantic segmentation to identify the lead vehicle in the image data as a vehicle object;

generate a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle;

generate a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle;

combine the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle;

adjust the velocity command using a dynamic gain, the dynamic gain being a function of a measured acceleration of the autonomous vehicle; and

control the autonomous vehicle to conform to the adjusted velocity command.

2. The system of claim 1 wherein the input object data includes distance data from one or more light imaging, detection, and ranging (LIDAR) sensors.

3. The system of claim 1 wherein the weighted distance differential is produced by multiplying a distance weight coefficient with the difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle.

4. The system of claim 1 wherein the weighted velocity differential is produced by multiplying a velocity weight coefficient with the difference between the velocity of the autonomous vehicle and a velocity of the lead vehicle.

5. The system of claim 1 further including summing the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle.

6. The system of claim 1 wherein controlling the autonomous vehicle further includes directing a vehicle control subsystem of the autonomous vehicle to cause the autonomous vehicle to achieve a speed corresponding to the velocity command.

7. The system of claim 1 wherein the adaptive cruise control module, executable by the data processor, is further configured to process the image data by semantic segmentation to identify the lead vehicle in the image data as a vehicle object and assign a categorical label to the vehicle object.

8. The system of claim 1 wherein the adaptive cruise control module, executable by the data processor, is further configured to adjust the velocity command using the dynamic gain, the dynamic gain being a function of a measured velocity and a measured acceleration of the autonomous vehicle.

9. A method comprising:

receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from a video stream generated by an image generating device, the input object data also including distance data and velocity data relative to a lead vehicle;

processing the image data by semantic segmentation to identify the lead vehicle in the image data as a vehicle object;

generating a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle;

generating a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle;

combining the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle;

adjusting the velocity command using a dynamic gain, the dynamic gain being a function of a measured acceleration of the autonomous vehicle; and

controlling the autonomous vehicle to conform to the adjusted velocity command.

10. The method of claim 9 wherein the input object data includes distance data from one or more light imaging, detection, and ranging (LIDAR) sensors.

11. The method of claim 9 wherein the weighted distance differential is produced by multiplying a distance weight coefficient with the difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle.

12. The method of claim 9 wherein the weighted velocity differential is produced by multiplying a velocity weight coefficient with the difference between the velocity of the autonomous vehicle and a velocity of the lead vehicle.

13. The method of claim 9 further including summing the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle.

14. The method of claim 9 wherein controlling the autonomous vehicle further includes directing a vehicle control subsystem of the autonomous vehicle to cause the autonomous vehicle to achieve a speed corresponding to the velocity command.

15. The method of claim 9 including processing the image data by semantic segmentation to identify the lead vehicle in the image data as a vehicle object and assigning a categorical label to the vehicle object.

16. The method of claim 9 including adjusting the velocity command using the dynamic gain, the dynamic gain being a function of a measured velocity and a measured acceleration of the autonomous vehicle.

17. A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to:

receive input object data from a subsystem of an autonomous vehicle, the input object data including image data from a video stream generated by an image generating device, the input object data also including distance data and velocity data relative to a lead vehicle;

process the image data by semantic segmentation to identify the lead vehicle in the image data as a vehicle object;

generate a weighted distance differential corresponding to a weighted difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle;

generate a weighted velocity differential corresponding to a weighted difference between a velocity of the autonomous vehicle and a velocity of the lead vehicle;

combine the weighted distance differential and the weighted velocity differential with the velocity of the lead vehicle to produce a velocity command for the autonomous vehicle;

adjust the velocity command using a dynamic gain, the dynamic gain being a function of a measured acceleration of the autonomous vehicle; and

control the autonomous vehicle to conform to the adjusted velocity command.

18. The non-transitory machine-useable storage medium of claim 17 wherein the input object data includes distance data from one or more light imaging, detection, and ranging (LIDAR) sensors.

19. The non-transitory machine-useable storage medium of claim 17 wherein the weighted distance differential is produced by multiplying a distance weight coefficient with the difference between an actual distance between the autonomous vehicle and the lead vehicle and a desired distance between the autonomous vehicle and the lead vehicle.

20. The non-transitory machine-useable storage medium of claim 17 wherein the weighted velocity differential is produced by multiplying a velocity weight coefficient with the difference between the velocity of the autonomous vehicle and a velocity of the lead vehicle.