Autonomous probability control

• US 10,026,317 B2
• Filed: 03/17/2016
• Issued: 07/17/2018
• Est. Priority Date: 02/25/2016
• Status: Expired due to Fees

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Perkins, William Paul, Miller, Kenneth James, Kelley, David B.
• Primary Examiner(s): ARTHUR JEANGLAUD, GERTRUDE
• Application Number:
  US15/073,137
• Publication Number:
  US 20170249844A1
• Time in Patent Office: 852 Days
• Field of Search: 701 25, 701 36, 701301, 340903
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2540/21 Voice
• CPC Class Codes: B60W 2540/225 Direction of gaze
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2556/65 Data transmitted between vehicles
• CPC Class Codes: B60W 2710/06 Combustion engines, Gas turbines
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2710/20 Steering systems
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 40/00 Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 60/0059 Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 17/87 Combinations of systems using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 17/931 : of land vehicles
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 2013/93272 : in the back of the vehicles
• CPC Class Codes: G01S 2013/93274 : on the side of the vehicles
• CPC Class Codes: G05D 1/0061 : for transition from automatic pilot to manual pilot and vice versa
• CPC Class Codes: G06F 17/00 : Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06V 20/59 : inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G07C 5/00 : Registering or indicating the working of vehicles
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

An action probability factor is developed based at least in part on a plurality of probability arrays predicting one or more probabilities of a deviation from at least one of a planned vehicle direction, position, speed, and acceleration. Levels of autonomous control are transitioned based at least in part on the action probability factor.

Claims

1. A method, comprising:

determining a plurality of probability arrays, each probability array predicting a probability of a deviation from at least one of a planned vehicle direction, position, speed, and acceleration;

developing an action probability factor indicating a probability that an action performed by a vehicle user was performed with intent to control a vehicle based at least in part on the plurality of probability arrays; and

transitioning between levels of autonomous control, including an autonomous, a semi-autonomous, and a manual level, based at least in part on the action probability factor.

2. The method of claim 1, wherein the autonomous level of control comprises little or no control of each of vehicle steering, braking, and propulsion by a vehicle operator, the semi-autonomous level of control includes partial or full control of at least one of vehicle steering, braking, and propulsion by the vehicle operator controller, and the manual level of control includes partial or full control of each of vehicle steering, braking, and propulsion by a vehicle operator.

3. The method of claim 1, wherein each probability array includes a plurality of probabilities.

4. The method of claim 1, further comprising changing from a first one of the levels of autonomous control to a second one of the levels of autonomous control when the action probability factor exceeds a first threshold.

5. The method of claim 1, further comprising actuating at least one of vehicle steering, braking, and propulsion according to the action probability factor.

6. The method of claim 1, further comprising identifying an object and adjusting the probability arrays based on at least one of the direction, position, speed, and acceleration of the object.

7. The method of claim 1, further comprising comparing the probability arrays to a predetermined safe state to determine the action probability factor.

8. The method of claim 7, wherein the predetermined safe state includes a peril factor for an object having a risk of collision with a vehicle.

9. The method of claim 1, wherein a probability array for a planned vehicle direction includes at least one of a route-based probability array, an object-based probability array, and a vehicle-based probability array.

10. An autonomous vehicle, comprising:

a plurality of sensors and receivers for receiving signals, from a plurality of sources, representing at least one of a planned vehicle direction, position, speed, and acceleration;

at least one controller programmed to determine a plurality of probability arrays, each probability array predicting a probability of a deviation from at least one of a planned vehicle direction, position, speed, and acceleration and to develop an action probability factor based on the plurality of probability arrays; and

a plurality of vehicle control subsystems;

wherein the at least one controller is programmed to transition control of the vehicle between levels of autonomous control, including an autonomous, a semi-autonomous, and a manual level, based at least in part on the action probability factor.

11. The vehicle of claim 10, wherein the autonomous level of control comprises little or no control of each of vehicle steering, braking, and propulsion by a vehicle operator, the semi-autonomous level of control includes partial or full control of at least one of vehicle steering, braking, and propulsion by the vehicle operator controller, and the manual level of control includes partial or full control of each of vehicle steering, braking, and propulsion by a vehicle operator.

12. The vehicle of claim 10, wherein each probability array includes a plurality of probabilities.

13. The vehicle of claim 10, wherein the controller is programmed to change from a first one of the levels of autonomous control to a second one of the levels of autonomous control when the action probability factor exceeds a first threshold.

14. The vehicle of claim 10, wherein the controller is programmed to actuate at least one of vehicle steering, braking, and propulsion according to the action probability factor.

15. The vehicle of claim 10, further comprising identifying an object and adjusting the probability arrays based on at least one of the direction, position, speed, and acceleration of the object.

16. The vehicle of claim 10, wherein the controller is programmed to compare the probability arrays to a predetermined safe state to determine the action probability factor.

17. The vehicle of claim 16, wherein the predetermined safe state includes a peril factor for an object having a risk of collision with a vehicle.

18. The vehicle of claim 10, wherein a probability array for a planned vehicle direction includes at least one of a route-based probability array, an object-based probability array, and a vehicle-based probability array.