Cloud-aided and collaborative data learning among autonomous vehicles to optimize the operation and planning of a smart-city infrastructure
Communication network architectures, systems, and methods for supporting a network of mobile nodes. As a non-limiting example, various aspects of this disclosure provide autonomous vehicle network architectures, systems, and methods for supporting a dynamically configurable network of autonomous vehicles comprising a complex array of both static and moving communication nodes, including systems and methods for collaborative data learning among autonomous vehicles.
1. A network of nodes, comprising:
a first node comprising:
a transceiver configured to receive data from one or more nodes of the network;
memory configured to store the received data; and
one or more processors configured to:
process the stored data; and
manage, based on the processing of the stored data, a data learning mechanism, wherein the managing comprises:
creating a previous data model as a copy of a present data model for infrastructure used by the one or more nodes;
generating a new data model based on the present data model and one or more inputs comprising information obtained based on the processing of the stored data;
evaluating the new data model and the previous data model; and
designating based on the evaluating one of the new data model and the previous data model as the present data model,
wherein the transceiver is configured to transmit the present data model to at least one of the one or more nodes when the new data model is designated as the present data model.
2. The network of claim 1 , wherein the first node is a cloud server.
3. The network of claim 1 , wherein the one or more nodes comprise at least one autonomous vehicle.
4. The network of claim 1 , wherein the one or more nodes comprise at least one fixed access point.
5. The network of claim 1 , wherein a second node is configured to perform one or both of: relay data from a third node to the first node, and relay data from the first node to the third node.
6. The network of claim 1 , wherein the data learning mechanism is one of a plurality of data learning mechanisms.
7. The network of claim 1 , wherein the data learning mechanism comprises one or more of: a machine learning mechanism, a deep learning mechanism, and another artificial intelligence mechanism.
8. The network of claim 1 , wherein the data learning mechanism comprises at least one support vector machine.
9. The network of claim 1 , wherein the one or more processors evaluate the new data model and the previous data model using ground truth data.
10. The network of claim 1 , wherein the new data model is output for use as the present data model when the new data model is evaluated to be at least as good as the previous data model.
11. The network of claim 1 , wherein the first node is an autonomous vehicle.
12. The network of claim 11 , wherein a processing module of the first node is configured to generate the new data model in concert with other nodes.
13. The network of claim 11 , wherein a processing module of the first node is configured to generate the new data model with one or both of: a fourth node and a fifth node, wherein the fourth node is an autonomous vehicle and the fifth node is a cloud server.
14. The network of claim 1 , wherein the first node is configured to provide updates for the present data model.
15. A method for providing a present data model by a first node in a network of nodes, comprising:
receiving, via a transceiver, data from one or more nodes of the network;
storing the received data in memory;
processing, by one or more processors, the stored data; and
managing, based on the processing of the stored data, a data learning mechanism, wherein the managing comprises:
creating a previous data model as a copy of a present data model for infrastructure used by the one or more nodes;
generating a new data model based on the present data model and one or more inputs comprising information obtained based on the processing of the stored data;
evaluating the new data model and the previous data model;
designating based on the evaluating one of the new data model and the previous data model as the present data model; and
when the new data model is designated as the present data model, transmitting the present data model to at least one of the one or more nodes.
16. The method of claim 15 , wherein the first node is a cloud server and the one or more nodes comprise at least one autonomous vehicle.
17. The method of claim 15 , wherein the data learning mechanism is one of a plurality of data learning mechanisms.
18. The method of claim 15 , wherein the data learning mechanism comprises one or more of: a deep learning mechanism, a machine learning mechanism, an artificial intelligence mechanism, and at least one support vector machine.
19. The method of claim 15 , comprising evaluating by the one or more processors the new data model and the previous data model using ground truth data.
20. The method of claim 15 , wherein the first node is an autonomous vehicle.