APPARATUS AND METHOD FOR ESTIMATING VEHICLE VELOCITY

• US 20150291178A1
• Filed: 07/24/2014
• Published: 10/15/2015
• Est. Priority Date: 04/10/2014
• Status: Abandoned Application

• Current Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Original Assignee: Hyundai Motor Company (Hyundai Motor Group)
• Inventors: You, Seung Han, Shin, Young Ho
• Application Number:
  US14/339,827
• Publication Number:
  US 20150291178A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2050/0022 Gains, weighting coefficients or weighting functions
• CPC Class Codes: B60W 2050/0033 Single-track, 2D vehicle model, i.e. two-wheel bicycle model
• CPC Class Codes: B60W 2420/905 the sensor being an xyz axis sensor
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/12 Lateral speed
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2520/16 Pitch
• CPC Class Codes: B60W 2520/18 Roll
• CPC Class Codes: B60W 2520/28 Wheel speed
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 40/103 Side slip angle of vehicle body
• CPC Class Codes: B60W 40/105 Speed
• CPC Class Codes: G01P 7/00 Measuring speed by integrating acceleration measuring travelled distance by double integration of acceleration G01C21/16

Abstract

An apparatus and a method for estimating a vehicle velocity are provided. The apparatus includes an inertia sensor that is configured to measure six degrees of freedom of a vehicle and a vehicle interior sensor that is configured to measure vehicle information. A processor is configured to estimate a kinematic model based longitudinal velocity and lateral velocity using the six degrees of freedom measured by the inertia sensor and estimate a physical model based lateral velocity and a wheel velocity based longitudinal velocity using the vehicle information. In addition, the processor is configured to estimate the vehicle velocity using the longitudinal velocity and lateral velocity.

Claims

1. An apparatus for estimating a vehicle velocity, the apparatus comprising:

an inertia sensor configured to measure six degrees of freedom of a vehicle;

a vehicle interior sensor configured to measure vehicle information;

a processor configured to;

estimate a kinematic model based longitudinal velocity and lateral velocity using the 6 degrees of freedom measured by the inertia sensor;

estimate a physical model based lateral velocity and a wheel velocity based longitudinal velocity using the vehicle information;

estimate the vehicle velocity using the longitudinal velocity and lateral velocity.

2. The apparatus according to claim 1, wherein the six degrees of freedom includes a longitudinal acceleration, a lateral acceleration, a vertical acceleration, a pitch rate, a yaw rate, and a roll rate.

3. The apparatus according to claim 1, wherein the inertia sensor includes:

an acceleration sensor configured to measure the longitudinal acceleration, the lateral acceleration, and the vertical acceleration, and

a gyro sensor configured to measure the pitch rate, the yaw rate, and the roll rate.

4. The apparatus according to claim 1, wherein the vehicle interior sensor includes:

a steering angle sensor configured to measure a steering angle, and

a wheel velocity sensor configured to measure a wheel velocity.

5. The apparatus according to claim 1, wherein the physical model is a single track model.

6. The apparatus according to claim 1, wherein the processor is further configured to:

estimate a vehicle lateral velocity and a lateral slip angle by combining a kinematic model based lateral velocity and a physical model based lateral velocity,

estimate a vehicle longitudinal velocity by combining the kinematic model based longitudinal velocity and the wheel velocity based longitudinal velocity,

assign weights to the kinematic model based lateral velocity and the physical model based lateral velocity depending on a driving situation, and

assign weights to a kinematic model based lateral velocity and a physical model based lateral velocity depending on the driving situation.

7. The apparatus according to claim 6, wherein the driving situation is classified into a non-linear tire friction interval and a linear tire friction interval.

8. The apparatus according to claim 6, wherein the processor is configured to set model weights based on a rear wheel slip angle, a lateral acceleration, a yaw rate error, a steering angle change rate, and an estimated divergence index.

9. The apparatus according to claim 6, wherein the processor is configured to set model weights based on a master cylinder pressure, a road friction coefficient, a pitch, a yaw rate, a lateral velocity, and a longitudinal acceleration.

10. A method for estimating a vehicle velocity, the method comprising:

measuring, by a sensor, six degrees of freedom and vehicle information;

estimating, by a processor, kinematic model based longitudinal velocity and lateral velocity, a physical model based lateral velocity, and a wheel velocity based longitudinal velocity using the six degrees of freedom and the vehicle information; and

estimating, by the processor, the vehicle velocity by combining a longitudinal velocity and lateral velocity estimated by the kinematic model, a lateral velocity estimated using the physical model, and a longitudinal velocity estimated using the wheel velocity.

11. The method of claim 10, wherein the six degrees of freedom includes a longitudinal acceleration, a lateral acceleration, a vertical acceleration, a pitch rate, a yaw rate, and a roll rate.

12. The method of claim 10, wherein the sensor includes an inertia sensor and a vehicle interior sensor.

13. The method of claim 12, wherein the inertia sensor includes:

an acceleration sensor configured to measure the longitudinal acceleration, the lateral acceleration, and the vertical acceleration, and

a gyro sensor configured to measure the pitch rate, the yaw rate, and the roll rate.

14. The method of claim 12, wherein the vehicle interior sensor includes:

a steering angle sensor configured to measure a steering angle, and

a wheel velocity sensor configured to measure a wheel velocity.

15. The method of claim 10, further comprising:

estimating, by the processor, a vehicle lateral velocity and a lateral slip angle by combining a kinematic model based lateral velocity and a physical model based lateral velocity,

estimating, by the processor, a vehicle longitudinal velocity by combining the kinematic model based longitudinal velocity and the wheel velocity based longitudinal velocity,

assigning, by the processor, weights to the kinematic model based lateral velocity and the physical model based lateral velocity depending on a driving situation, and

assigning, by the processor, weights to a kinematic model based lateral velocity and a physical model based lateral velocity depending on the driving situation.

16. The method of claim 15, wherein the driving situation is classified into a non-linear tire friction interval and a linear tire friction interval.

17. A non-transitory computer readable medium containing program instructions executed by a processor, the computer readable medium comprising:

program instructions that control a sensor to measure six degrees of freedom and vehicle information;

program instructions that estimate kinematic model based longitudinal velocity and lateral velocity, a physical model based lateral velocity, and a wheel velocity based longitudinal velocity using the six degrees of freedom and the vehicle information; and

program instructions that estimate the vehicle velocity by combining a longitudinal velocity and lateral velocity estimated by the kinematic model, a lateral velocity estimated using the physical model, and a longitudinal velocity estimated using the wheel velocity.

18. The non-transitory computer readable medium of claim 17, wherein the six degrees of freedom includes a longitudinal acceleration, a lateral acceleration, a vertical acceleration, a pitch rate, a yaw rate, and a roll rate.

19. The non-transitory computer readable medium of claim 17, further comprising:

program instructions that estimate a vehicle lateral velocity and a lateral slip angle by combining a kinematic model based lateral velocity and a physical model based lateral velocity,

program instructions that estimate a vehicle longitudinal velocity by combining the kinematic model based longitudinal velocity and the wheel velocity based longitudinal velocity,

program instructions that assign weights to the kinematic model based lateral velocity and the physical model based lateral velocity depending on a driving situation, and

program instructions that assign weights to a kinematic model based lateral velocity and a physical model based lateral velocity depending on the driving situation.

20. The non-transitory computer readable medium of claim 19, wherein the driving situation is classified into a non-linear tire friction interval and a linear tire friction interval.