Student-T process personalized adaptive cruise control

1. A vehicle comprising:

a controller programed to:

collect a set of data related to a driver of the vehicle;

predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model;

generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data;

transmit incremental learning related to the updated STP ML model to a server; and

receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning,

wherein the incremental learning comprises a machine learning algorithm to dynamically train the cloud STP ML model based on the initial STP ML model and the updated STP ML model.

2. The vehicle of claim 1 , wherein the set of data comprises a velocity of the vehicle, a velocity of another vehicle preceding the vehicle, and a distance between the vehicle and the another vehicle.

3. The vehicle of claim 1 , wherein the personalized driving setting for the driver is a personalized adaptive cruise control setting for the driver.

4. The vehicle of claim 1 , wherein the controller is further programmed to determine an acceleration profile for the vehicle based on the personalized driving setting.

5. The vehicle of claim 1 , wherein the controller is further programmed to update the personalized driving setting based on driving preferences by the driver.

6. The vehicle of claim 1 , wherein the controller is further programmed to update the personalized driving setting based on driving conditions.

7. The vehicle of claim 6 , wherein the driving conditions include at least one of weather information, a type of a road on which the vehicle is driving, a surface condition of the road on which the vehicle is driving, and a degree of traffic on the road on which the vehicle is driving.

8. The vehicle of claim 1 , wherein the controller is configured to determine a target gap between the vehicle and a leading vehicle based on the personalized driving setting, a current gap between the vehicle and the leading vehicle, and a relative velocity between the vehicle and the leading vehicle.

9. The vehicle of claim 1 , wherein the controller is further configured to operate the vehicle based on the personalized driving setting.

10. A method for generating a personalized driving setting using a student-T process (STP) machine learning (ML) model, the method comprising:

collecting a set of data related to a driver of a vehicle;

predicting a driving setting for the driver using the set of data and an initial STP ML model;

generating an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data;

transmitting incremental learning related to the updated STP ML model to a server; and

receiving, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning,

wherein the incremental learning comprises a machine learning algorithm to dynamically train the cloud STP ML model based on the initial STP ML model and the updated STP ML model.

11. The method of claim 10 , wherein the set of data comprises a velocity of the vehicle, a velocity of another vehicle preceding the vehicle, and a distance between the vehicle and the another vehicle.

12. The method of claim 10 , wherein the personalized driving setting for the driver is a personalized adaptive cruise control setting for the driver.

13. The method of claim 10 , further comprising determining an acceleration profile for the vehicle based on the personalized driving setting.

14. The method of claim 10 , further comprising updating the personalized driving setting based on driving preferences by the driver.

15. The method of claim 10 , further comprising updating the personalized driving setting based on driving conditions.

16. The method of claim 15 , wherein the driving conditions include at least one of weather information, a type of a road on which the vehicle is driving, a surface condition of the road on which the vehicle is driving, and a degree of traffic on the road on which the vehicle is driving.

17. The method of claim 10 , further comprising determining a target gap between the vehicle and a leading vehicle based on the personalized driving setting, a current gap between the vehicle and the leading vehicle, and a relative velocity between the vehicle and the leading vehicle.

18. A system for generating a personalized driving setting using an student-T process (STP) machine learning (ML) model, the system comprising a vehicle and a server, wherein:

the vehicle is programed to:

collect a set of data related to a driver of the vehicle;

predict a driving setting for the driver using the set of data and an initial STP ML model;

generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; and

transmit incremental learning related to the updated STP ML model to the server; and

the server is programed to:

train a cloud STP ML model based on the incremental learning comprising a machine learning algorithm that dynamically trains the cloud STP ML model based on the initial STP ML model and the updated STP ML model; and

transmit, to the vehicle, a personalized driving setting for the driver based on the trained cloud STP ML model.

19. The system of claim 18 , wherein:

the set of data comprises a velocity of the vehicle, a velocity of another vehicle preceding the vehicle, and a distance between the vehicle and the another vehicle; and

the personalized driving setting for the driver is a personalized adaptive cruise control setting for the driver.

20. The system of claim 18 , wherein the vehicle is further configured to determine a target gap between the vehicle and a leading vehicle based on the personalized driving setting, a current gap between the vehicle and the leading vehicle, and a relative velocity between the vehicle and the leading vehicle.