SYSTEM AND METHOD FOR UPDATING A ROS NODE IN A CONVOLUTIONAL NEURAL NETWORK
Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.
1 . A method comprising:
receiving an update associated with a first autonomous vehicle model;
determining one or more nodes of the first autonomous vehicle model that are to be updated based on the received update; and
applying the update to the determined one or more nodes of the first autonomous vehicle model to generate a second autonomous vehicle model.
2 . The method of claim 1 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes replacing the determined one or more nodes of the first autonomous vehicle model.
3 . The method of claim 1 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes changing a parameter of the determined one or more nodes of the first autonomous vehicle model.
4 . The method of claim 1 , wherein a first node of the one or more nodes is associated with at least one of changing a steering angle and/or changing a velocity of an autonomous vehicle.
5 . The method of claim 4 , wherein a second node of the one or more nodes is associated with at least one of receiving an image, processing the image, identifying one or more objects in the image, and/or tracking the identified one or more objects in the image.
6 . The method of claim 4 , wherein a second node of the one or more nodes is associated with at least one of determining a path for an autonomous vehicle to follow and/or determining an operating mode for an autonomous vehicle.
7 . The method of claim 1 , wherein determining one or more nodes of the first autonomous vehicle model that are to be updated is based on at least one of a location or a time associated with the one or more nodes.
8 . The method of claim 1 , further comprising:
providing the second autonomous vehicle model to an autonomous vehicle.
9 . The method of claim 8 , further comprising:
providing the second autonomous vehicle model to a second autonomous vehicle.
10 . The method of claim 1 , wherein the second autonomous vehicle model is generated at an autonomous vehicle.
11 . A system comprising:
a memory;
a processor in communication with the memory, wherein the processor executes instructions stored in the memory, which cause the processor to execute a method, the method comprising:
receiving an update associated with a first autonomous vehicle model;
determining one or more nodes of the first autonomous vehicle model that are to be updated based on the received update; and
applying the update to the determined one or more nodes of the first autonomous vehicle model to generate a second autonomous vehicle model.
12 . The system of claim 11 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes replacing the determined one or more nodes of the first autonomous vehicle model.
13 . The system of claim 11 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes changing a parameter of the determined one or more nodes of the first autonomous vehicle model.
14 . The system of claim 11 , wherein a first node of the one or more nodes is associated with at least one of changing a steering angle and/or changing a velocity of an autonomous vehicle.
15 . The system of claim 14 , wherein a second node of the one or more nodes is associated with at least one of receiving an image, processing the image, identifying one or more objects in the image, and/or tracking the identified one or more objects in the image.
16 . A non-transitory computer readable medium having stored thereon instructions, which when executed by a processor cause the processor to execute a method, the method comprising:
receiving an update associated with a first autonomous vehicle model;
determining one or more nodes of the first autonomous vehicle model that are to be updated based on the received update; and
applying the update to the determined one or more nodes of the first autonomous vehicle model to generate a second autonomous vehicle model.
17 . The non-transitory computer readable medium of claim 16 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes replacing the determined one or more nodes of the first autonomous vehicle model.
18 . The non-transitory computer readable medium of claim 16 , wherein applying the update to the determined one or more nodes of the first autonomous vehicle model includes changing a parameter of the determined one or more nodes of the first autonomous vehicle model.
19 . The non-transitory computer readable medium of claim 16 , wherein a first node of the one or more nodes is associated with at least one of changing a steering angle and/or changing a velocity of an autonomous vehicle.
20 . The non-transitory computer readable medium of claim 19 , wherein a second node of the one or more nodes is associated with at least one of receiving an image, processing the image, identifying one or more objects in the image, and/or tracking the identified one or more objects in the image.