Autonomous vehicle using path prediction

• US 10,118,610 B2
• Filed: 08/31/2016
• Issued: 11/06/2018
• Est. Priority Date: 08/31/2016
• Status: Active Grant

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Deng, Kun, Luo, Wangdong, Liu, Nanjun, Miller, Alex Maurice
• Primary Examiner(s): Dager, Jonathan M
• Assistant Examiner(s): Evans, Garrett F
• Application Number:
  US15/252,425
• Publication Number:
  US 20180056995A1
• Time in Patent Office: 797 Days
• Field of Search: None
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/184 with wheel brakes
• CPC Class Codes: B60W 10/20 including control of steering systems
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2520/06 Direction of travel
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2556/00 Input parameters relating to data
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2710/20 Steering systems
• CPC Class Codes: B60W 2900/00 Indexing codes relating to the purpose of, or problem solved of road vehicle drive control systems not otherwise provided for in groups B60W30/00
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B62D 15/025 Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation B62D1/28 takes precedence
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A controller in a host vehicle includes a processor and a memory storing processor-executable instructions. The processor is programmed to calculate an error bound for confidence intervals of a predicted position at a future time of a target vehicle relative laterally to a current position and orientation of the host vehicle at a current time based on a current position and velocity of the target vehicle and the future time.

Claims

1. A controller comprising a processor and a memory storing processor-executable instructions, wherein the processor is programmed to:

calculate an error bound for confidence intervals of a predicted position of a target vehicle; and

instruct a steering system of a host vehicle to turn based on the error bound;

wherein the predicted position is at a future time;

the predicted position is relative laterally to a current position and orientation of the host vehicle at a current time;

calculating the error bound is based on a current position and velocity of the target vehicle and the future time; and

the error bound is a product of the future time and a value that is constant with respect to the future time.

2. The controller of claim 1, wherein the error bound is further based on a current velocity and yaw rate of the host vehicle.

3. The controller of claim 2, wherein the error bound is further based on a relative heading angle between the host and target vehicles.

4. The controller of claim 3, wherein the error bound has a linear relationship with the future time.

5. The controller of claim 4, wherein the error bound is determined from an equation:

ε

(t,s)=b(t)*(s−

t)+c(t), in which

6. The controller of claim 3, wherein the error bound is based on inputs consisting of the current position and velocity of the target vehicle, the future time, the current velocity and yaw rate of the host vehicle, and the relative heading angle between the host and target vehicles.

7. The controller of claim 1, wherein the processor is further programmed to calculate the predicted position at the future time.

8. The controller of claim 7, wherein the processor is programmed to generate coefficients to calculate the predicted position at the future time using a quadratic polynomial method.

9. The controller of claim 7, wherein the processor is further programmed to control a vehicle subsystem to evade the target vehicle based on the predicted position and the error bound.

10. The controller of claim 9, wherein controlling the vehicle subsystem includes instructing brakes to brake.

11. The controller of claim 9, wherein controlling the vehicle subsystem includes instructing the steering system to turn.

12. The controller of claim 1, wherein the processor is further programmed to calculate an error metric equal to an absolute value of a difference of the predicted position and a measured position of the target vehicle;

wherein the measured position is at the future time; and

the measured position is relative laterally to the current position and orientation of the host vehicle.

13. The controller of claim 12, wherein the processor is further programmed to calculate a quality measure equal to a minimum value of a lesser value of 1 and a ratio of the error bound to the error metric, wherein the minimum value is calculated over a period.

14. The controller of claim 13, wherein the processor is further programmed to disable a feature relying on the error bound based on the quality measure.

15. The controller of claim 14, wherein the feature is one of automated braking, automated steering, and autonomous operation.

16. A method comprising:

calculating an error bound for confidence intervals of a predicted position of a target vehicle; and

instructing a steering system of a host vehicle to turn based on the error bound;

wherein the predicted position is at a future time;

the predicted position is relative laterally to a current position and orientation of the host vehicle at a current time;

calculating the error bound is based on a current position and velocity of the target vehicle and the future time; and

the error bound is a product of the future time and a value that is constant with respect to the future time.

17. The method of claim 16, wherein the error bound is further based on a current velocity and yaw rate of the host vehicle and a relative heading angle between the host and target vehicles.

18. The method of claim 17, further comprising calculating an error metric equal to an absolute value of a difference of the predicted position and a measured position of the target vehicle;

wherein the measured position is at the future time; and

the measured position is relative laterally to the current position and orientation of the host vehicle.

19. The method of claim 18, further comprising calculating a quality measure equal to a minimum value of a lesser value of 1 and a ratio of the error bound to the error metric, wherein the minimum value is calculated over a period.

20. The method of claim 19, further comprising adjusting a path prediction algorithm based on the quality measure.