Systems and methods for obstacle detection
A vehicle control system for an agricultural vehicle including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive (i) image data depicting at least a portion of a hazard area associated with the agricultural vehicle and (ii) ultrasonic sensor data from an ultrasonic sensor monitoring the hazard area, determine, based on a combination of the image data and the ultrasonic sensor data, whether an obstacle is positioned at least partially within the hazard area, and perform an action with respect to the agricultural vehicle in response to a determination that the obstacle is positioned at least partially within the hazard area.
1. A vehicle control system for an agricultural vehicle, the vehicle control system comprising:
a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to:
receive (i) image data depicting at least a portion of a hazard area associated with the agricultural vehicle and (ii) ultrasonic sensor data from an ultrasonic sensor monitoring the hazard area, wherein the hazard area is dynamically updated based on a change in an implement operating mode of the agricultural vehicle;
determine, based on a combination of the image data and the ultrasonic sensor data, whether an obstacle is positioned at least partially within the hazard area; and
perform an action with respect to the agricultural vehicle in response to a determination that the obstacle is positioned at least partially within the hazard area.
2. The vehicle control system of claim 1 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes performing background subtraction using the image data and a background model.
3. The vehicle control system of claim 2 , wherein performing background subtraction using the image data includes labeling one or more pixels of the image data as foreground pixels and one or more pixels of the image data as background pixels.
4. The vehicle control system of claim 1 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes executing a machine learning model using the image data to identify an object in the image data.
5. The vehicle control system of claim 4 , wherein determining whether the obstacle is positioned at least partially within the hazard area further includes determining a position of the object in the image data using the ultrasonic sensor data.
6. The vehicle control system of claim 1 , wherein the instructions further cause the processing circuit to receive a signal to remotely start the agricultural vehicle, and wherein performing the action includes transmitting an image showing the obstacle positioned at least partially within the hazard area.
7. The vehicle control system of claim 1 , wherein the instructions further cause the processing circuit to receive a signal to remotely start the agricultural vehicle, and wherein performing the action includes preventing the agricultural vehicle from starting.
8. A method for controlling an agricultural vehicle, comprising:
receiving (i) image data depicting at least a portion of a hazard area associated with the agricultural vehicle and (ii) ultrasonic sensor data from an ultrasonic sensor monitoring the hazard area, wherein the hazard area is dynamically updated based on a change in an implement operating mode of the agricultural vehicle;
determining, based on a combination of the image data and the ultrasonic sensor data, whether an obstacle is positioned at least partially within the hazard area; and
performing an action with respect to the agricultural vehicle in response to a determination that the obstacle is positioned at least partially within the hazard area.
9. The method of claim 8 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes performing background subtraction using the image data and a background model.
10. The method of claim 9 , wherein performing the background subtraction using the image data includes labeling one or more pixels of the image data as foreground pixels and one or more pixels of the image data as background pixels.
11. The method of claim 8 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes executing a machine learning model using the image data to identify an object in the image data.
12. The method of claim 11 , wherein determining whether the obstacle is positioned at least partially within the hazard area further includes determining a position of the object in the image data using the ultrasonic sensor data.
13. The method of claim 8 , further comprising receiving a signal to remotely start the agricultural vehicle, and wherein performing the action includes transmitting an image showing the obstacle positioned at least partially within the hazard area.
14. The method of claim 8 , further comprising receiving a signal to remotely start the agricultural vehicle, and wherein performing the action includes preventing the agricultural vehicle from starting.
15. A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
receive (i) image data depicting at least a portion of a hazard area associated with an agricultural vehicle and (ii) ultrasonic sensor data from an ultrasonic sensor monitoring the hazard area, wherein the hazard area is dynamically updated based on a change in an implement operating mode of the agricultural vehicle;
determine, based on a combination of the image data and the ultrasonic sensor data, whether an obstacle is positioned at least partially within the hazard area; and
perform an action with respect to the agricultural vehicle in response to a determination that the obstacle is positioned at least partially within the hazard area, wherein the action includes preventing the agricultural vehicle from starting.
16. The non-transitory computer-readable storage medium of claim 15 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes performing background subtraction using the image data and a background model.
17. The non-transitory computer-readable storage medium of claim 16 , wherein performing the background subtraction using the image data includes labeling one or more pixels of the image data as foreground pixels and one or more pixels of the image data as background pixels.
18. The non-transitory computer-readable storage medium of claim 15 , wherein determining whether the obstacle is positioned at least partially within the hazard area includes executing a machine learning model using the image data to identify an object in the image data.
19. The non-transitory computer-readable storage medium of claim 18 , wherein determining whether the obstacle is positioned at least partially within the hazard area further includes determining a position of the object in the image data using the ultrasonic sensor data.
20. The non-transitory computer-readable storage medium of claim 15 , wherein the instructions further cause the one or more processors to receive a signal to remotely start the agricultural vehicle, and wherein performing the action includes transmitting an image showing the obstacle positioned at least partially within the hazard area.