Student-T process personalized adaptive cruise control

Patent metadata

Field	Value
Publication number	US-12036987-B2
Application number	US-202117388488-A
Country	US
Kind code	B2
Filing date	Jul 29, 2021
Priority date	Jul 29, 2021
Publication date	Jul 16, 2024
Grant date	Jul 16, 2024

Abstract

A vehicle includes 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.

First claim

What is claimed is:

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.

Assignees

• Toyota Eng & Mfg North America

Inventors

• Gupta Rohit
• Wang Yanbing
• Wang Ziran
• Han Kyungtae
• Tiwari Prashant

Classifications

• G07C5/008

  communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

• B60W2556/45

  External transmission of data to or from the vehicle · CPC title

• B60W2554/804

  Relative longitudinal speed · CPC title

• B60W2540/30

  Driving style · CPC title

• B60W2554/802

  Longitudinal distance · CPC title