Method and apparatus for managing vehicle's resource in autonomous driving system

1. A method for managing a resource in a vehicle, comprising:

executing at least one application requiring a resource in a first mode, the at least one application associated with an autonomous driving process of the vehicle;

obtaining driving route information;

obtaining, from a position data generation device disposed at the vehicle, location information providing a current location of the vehicle;

predicting resource utilization expected to be required in the first mode by using the driving route information and the location information;

determining a specific point for switching from the first mode executing the at least one application to a second mode;

receiving, from a server, a container module including application data of the second mode that includes information related to the at least one application to be operated in the vehicle; and

installing the received container module,

wherein the at least one application is executed in the second mode requiring less resources than being executed in the first mode.

2. The method of claim 1 , wherein predicting resource utilization includes:

applying the driving route information and the location information to an artificial neural network (ANN) model; and

predicting the resource utilization from an output value of the artificial neural network model.

3. The method of claim 2 , wherein predicting resource utilization includes:

inputting the driving route information and the location information into the artificial neural network model; and

predicting resource utilization related to the output value of the artificial neural network model.

4. The method of claim 3 , wherein inputting the driving route information into the artificial neural network model includes:

extracting a feature from the driving route information; and

inputting the extracted feature into the artificial neural network model.

5. The method of claim 4 ,

wherein the extracted feature includes a vector that is extracted from the driving route information based on a pre-trained embedding model, and

wherein the pre-trained embedding model is configured to vectorize the driving route information to an embedding vector related to the driving route information.

6. The method of claim 2 , further comprising:

applying an application usage history of passenger to the artificial neural network model as learning data.

7. The method of claim 1 , wherein the driving route information includes at least one of a driving route to a destination, execution mode information of the at least one application used in the autonomous driving process, a uniformity of a road surface located on the driving route, a curvature of a road, a number of surrounding objects, or a type of the surrounding objects.

8. The method of claim 1 , wherein the at least one application comprises at least one of a navigation application, an object detection application, a voice recognition application, or a multimedia application.

9. The method of claim 1 , wherein the server is configured to search for the application data based on allowable resource information for the vehicle.

10. The method of claim 1 , wherein the server is configured to search for the at least one application including a feature function that is included in the application data.

11. The method of claim 1 , wherein the container module further includes metadata corresponding to a specific function of the vehicle, and

wherein installing the received container module comprises installing the at least one application included in the received container module as an application corresponding to the specific function of the vehicle.

12. The method of claim 1 , further comprising:

determining a value of communication specification based on at least one of a traffic density, speed, or a presence or absence of obstacles,

wherein determining the specific point comprises, based on a determination that the value of communication specification has passed a predetermined threshold, determining a current time as the specific point.

13. The method of claim 1 ,

wherein determining the specific point comprises determining a point at which the resource utilization exceeds a first threshold as the specific point,

wherein installing the received container module comprises switching, based on the vehicle passing the specific point, the execution mode from the first mode executing the at least one application to the second mode.

14. An apparatus for managing a resource in a vehicle, comprising:

a transceiver configured to receive driving route information of the vehicle;

a position data generation device configured to provide location information providing a current location of the vehicle; and

a processor configured to:

execute at least one application requiring a resource in a first mode, the at least one application associated with an autonomous driving process of the vehicle,

predict resource utilization expected to be required in the first mode by using the received driving route information and the provided location information,

determine a specific point for switching from the first mode executing the at least one application to a second mode,

receive, from a server, a container module including application data of the second mode that includes information related to the at least one application to be operated in the vehicle, and

install the received container module,

wherein the at least one application is executed in the second mode requiring less resources than being executed in the first mode.

15. The apparatus of claim 14 , wherein predicting resource utilization includes:

applying the driving route information and the location information to an artificial neural network model, and

predicting the resource utilization from an output value of the artificial neural network model.

16. The apparatus of claim 15 , wherein predicting resource utilization includes:

inputting the driving route information and the location information into the artificial neural network model, and

predicting resource utilization related to the output value of the artificial neural network model.

17. The apparatus of claim 16 , wherein inputting the driving route information into the artificial neural network model includes:

extracting a feature from the driving route information, and

inputting the extracted feature into the artificial neural network model.

18. The apparatus of claim 17 ,

wherein the extracted feature includes a vector that is extracted from the driving route information based on a pre-trained embedding model, and

wherein the pre-trained embedding model is configured to vectorize the driving route information to an embedding vector related to the driving route information.

19. The apparatus of claim 15 , wherein the driving route information includes at least one of a driving route to a destination, execution mode information of the at least one application used in the autonomous driving process, a uniformity of a road surface located on the driving route, a curvature of a road, a number of surrounding objects, or a type of the surrounding objects.

20. The apparatus of claim 14 , wherein the at least one application comprises at least one of a navigation application, an object detection application, a voice recognition application, or a multimedia application.

21. The apparatus of claim 14 , wherein the server is configured to search for the application data based on allowable resource information for the vehicle.

22. The apparatus of claim 14 , wherein the server is configured to search for the at least one application including a feature function that is included in the application data.

23. The apparatus of claim 14 , wherein the container module further includes metadata corresponding to a specific function of the vehicle, and

wherein installing the received container module comprises installing the at least one application included in the received container module as an application corresponding to the specific function of the vehicle.

24. The apparatus of claim 14 , wherein the processor is further configured to:

determine a value of communication specification based on at least one of a traffic density, speed, or a presence or absence of obstacles; and

wherein determining the specific point comprises, based on a determination that the value of communication specification has passed a predetermined threshold, determining a current time as the specific point.

25. The apparatus of claim 14 ,

wherein determining the specific point comprises determining a point at which the resource utilization exceeds a first threshold as the specific point, and

wherein installing the received container module comprises switching, based on the vehicle passing the specific point, the execution mode from the first mode executing the at least one application to the second mode.