Determining when to drive autonomously

• US 8,718,861 B1
• Filed: 04/11/2012
• Issued: 05/06/2014
• Est. Priority Date: 04/11/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Montemerlo, Michael Steven, Urmson, Christopher Paul, Dolgov, Dmitri A., Nemec, Philip, Murveit, Hyman Jack
• Primary Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US13/444,215
• Time in Patent Office: 755 Days
• Field of Search: 701 23- 26, 701408-411, 701/532, 701/533
• US Class Current: 701/26
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• CPC Class Codes: B60W 2050/143 Alarm 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 2520/14 Yaw
• CPC Class Codes: B60W 2520/16 Pitch
• CPC Class Codes: B60W 2520/18 Roll
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/20 Road profile, i.e. the change in elevation or curvature of a plurality of continuous road segments
• CPC Class Codes: B60W 2552/25 Road altitude
• CPC Class Codes: B60W 2552/45 Pedestrian sidewalk
• CPC Class Codes: B60W 2552/50 Barriers
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/408 Traffic behavior, e.g. swarm
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2554/80 : Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2554/802 : Longitudinal distance
• CPC Class Codes: B60W 2555/60 : Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/40 : High definition maps
• CPC Class Codes: B60W 2556/50 : of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2720/24 : Direction of travel
• CPC Class Codes: B60W 30/00 : Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: B60W 30/182 : Selecting between different operative modes, e.g. comfort and performance modes
• CPC Class Codes: B60W 50/082 : Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 60/00276 : for two or more other traffic participants
• CPC Class Codes: B60W 60/0053 : from vehicle to occupant
• CPC Class Codes: B60W 60/0061 : Aborting handover process
• CPC Class Codes: G05D 1/0061 : for transition from automatic pilot to manual pilot and vice versa

Abstract

Aspects of the disclosure relate generally to determining whether an autonomous vehicle should be driven in an autonomous or semiautonomous mode (where steering, acceleration, and braking are controlled by the vehicle'"'"'s computer). For example, a computer may maneuver a vehicle in an autonomous or a semiautonomous mode. The computer may continuously receive data from one or more sensors. This data may be processed to identify objects and the characteristics of the objects. The detected objects and their respective characteristics may be compared to a traffic pattern model and detailed map information. If the characteristics of the objects deviate from the traffic pattern model or detailed map information by more than some acceptable deviation threshold value, the computer may generate an alert to inform the driver of the need to take control of the vehicle or the computer may maneuver the vehicle in order to avoid any problems.

Claims

1. A method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining, by a processor, a deviation value for the detected object based on a comparison of the characteristic for the detected object to traffic pattern model information, the traffic pattern model information including an expected range of values for a characteristic of objects in the road;

comparing the deviation value to a threshold deviation value for an expected range of values for the characteristic; and

when the deviation value is outside of the threshold deviation value, providing a notification to a driver of the vehicle.

2. The method of claim 1, further comprising receiving input from the driver indicating that the driver has taken control of the vehicle.

3. The method of claim 1, wherein the characteristic includes a position of the detected object and the deviation value is determined by calculating a difference between the position of the detected object and an expected range of values for position defined in the traffic pattern model information.

4. The method of claim 1, wherein the characteristic includes a speed of the detected object and the deviation value is determined by calculating a difference between the speed of the detected object and an expected range of values for speed defined in the traffic pattern model information.

5. The method of claim 1, wherein the characteristic includes a trajectory of the detected object and the deviation value is determined by calculating a difference between the trajectory of the detected object and an expected range of values for trajectory defined in the traffic pattern model information.

6. The method of claim 1, further comprising:

detecting a second object and a second characteristic for the second detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic for the second detected object to the traffic pattern model information;

comparing the second deviation value to the threshold deviation value for an expected range of values for the second characteristic; and

when the deviation value is within the threshold deviation value and the second deviation value is outside of a second threshold deviation value, providing the notification to the driver the vehicle.

7. The method of claim 1, further comprising:

detecting a second object and a second characteristic for the detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic for the second detected object to the traffic pattern model information; and

before providing the notification, determining whether the second deviation value is within a second threshold deviation value based on a comparison of the second deviation value to the second threshold deviation value.

8. The method of claim 1, further comprising:

determining a second deviation value for the detected object based on a comparison of the characteristic for the detected object to detailed map information describing expected features of the road and characteristics of the expected features;

comparing the second deviation value to a second threshold deviation value for the expected characteristics of the expected features; and

when the second deviation value is outside of the second threshold deviation value, providing the notification to the driver of the vehicle.

9. A method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining, by a processor, a deviation value for the detected object based on a comparison of the characteristic to detailed map information describing expected features of the road and characteristics of the expected features;

comparing the deviation value to a threshold deviation value for the expected characteristics of the expected features; and

when the deviation value is outside of the threshold deviation value, providing a notification to the driver of the vehicle.

10. The method of claim 9, further comprising receiving input from the driver indicating that the driver has taken control of the vehicle.

11. The method of claim 9, wherein the characteristic includes a position of the detected object and the deviation value is determined by calculating a difference between the position of the detected object and an expected characteristic for position defined in the detailed map information.

12. The method of claim 9, wherein the characteristic includes a shape of the detected object and the deviation value is determined by calculating a difference between the shape of the detected object and an expected characteristic for shape defined in the detailed map information.

13. The method of claim 9, wherein the characteristic includes a size of the detected object and the deviation value is determined by calculating a difference between the size of the detected object and an expected characteristic for size defined in the detailed map information.

14. The method of claim 9, further comprising:

detecting a second object and a second characteristic for the second detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic and the detailed map information;

comparing the second deviation value to a second threshold deviation value for the expected characteristics of the expected features; and

when the deviation value is within the threshold deviation value and the second deviation value is outside of the second threshold deviation value, providing a notification to the driver of the vehicle.

15. The method of claim 9, further comprising:

detecting a second object and a second characteristic for the second detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic and the detailed map information; and

before providing the notification, determining whether the second deviation value is within a second threshold deviation value.

16. A method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining, by a processor, a deviation value for the detected object based on a comparison of the characteristic and traffic pattern model information, the traffic pattern model information including an expected range of values for a characteristic of objects in the road;

comparing the deviation value to a threshold deviation value for the expected range of values for the characteristic of the given object; and

when the deviation value is outside of the threshold deviation value, maneuvering, without input from a driver, the vehicle defensively.

17. The method of claim 16, wherein maneuvering the vehicle defensively includes slowing the vehicle down.

18. The method of claim 16, wherein maneuvering the vehicle defensively includes changing lanes.

19. The method of claim 16, wherein maneuvering the vehicle defensively includes increasing the distance between the vehicle and another object.

20. The method of claim 16, wherein the characteristic includes a position of the detected object and the deviation value is determined by calculating a difference between the position of the detected object and an expected range of values for position defined in the traffic pattern model information.

21. The method of claim 16, wherein the characteristic includes a speed of the detected object and the deviation value is determined by calculating a difference between the speed of the detected object and an expected range of values for speed defined in the traffic pattern model information.

22. The method of claim 16, wherein the characteristic includes a trajectory of the detected object and the deviation value is determined by calculating a difference between the trajectory of the detected object and an expected range of values for trajectory defined in the traffic pattern model information.

23. The method of claim 16, further comprising:

detecting a second object and a second characteristic for the detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic to the traffic pattern model information;

comparing the second deviation value to a second threshold deviation value for the expected range of values for the characteristic of the given object; and

when the deviation value is within the threshold deviation value and the second deviation value is outside of the second threshold deviation value, maneuvering the vehicle defensively.

24. The method of claim 16, further comprising:

detecting a second object and a second characteristic for the detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic and the traffic pattern model information; and

before maneuvering the vehicle defensively, determining whether the second deviation value is within a second threshold deviation value based on a comparison of the second deviation value to the second threshold deviation value.

25. The method of claim 16, further comprising:

determining a second deviation value for the detected object based on a comparison of the characteristic for the detected object and detailed map information describing expected features of the road and characteristics of the expected features;

comparing the second deviation value to a second threshold deviation value for the expected characteristics of the expected features;

when the second deviation value is outside of the second threshold deviation value, maneuvering the vehicle defensively.

26. A method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining, by a processor, a deviation value for the detected object based on a comparison of the characteristic for the detected object and detailed map information describing expected features of the road and characteristics of the expected features;

comparing the deviation value to a threshold deviation value for the expected characteristics of the expected features; and

identifying a mismatched area when the deviation value is outside of the threshold deviation value.

27. The method of claim 26, wherein the characteristic includes a position of the detected object and the deviation value is determined by calculating a difference between the position of the detected object and an expected characteristic for position defined in the detailed map information.

28. The method of claim 26, wherein the characteristic includes a shape of the detected object and the deviation value is determined by calculating a difference between the shape of the detected object and an expected characteristic for shape defined in the detailed map information.

29. The method of claim 26, wherein the characteristic includes a size of the detected object and the deviation value is determined by calculating a difference between the size of the detected object and an expected characteristic for size defined in the detailed map information.

30. The method of claim 26, further comprising:

detecting a second object and a second characteristic for the detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic and the detailed map information;

comparing the second deviation value to a second threshold deviation value for the expected characteristics of the expected features;

when the deviation value is within the threshold deviation value and the second deviation value is outside of the second threshold deviation value, identifying a mismatched area; and

maneuvering, without input from the driver, the vehicle to avoid the mismatched area.

31. The method of claim 26, further comprising:

detecting a second object and a second characteristic for the detected object based on the received data;

determining a second deviation value for the second detected object based on a comparison of the second characteristic and the detailed map information; and

before identifying the mismatched area, determining whether the second deviation value is within the second threshold deviation value.

32. A device comprising:

memory storing traffic pattern model information including an expected range of values for a characteristic of objects in the road;

a processor coupled to the memory, the processor configured to;

receive data from one or more sensors associated with a vehicle;

detect an object and a characteristic for the detected object based on the received data;

determine a deviation value for the detected object based on a comparison of the characteristic for the detected object to the traffic pattern model information;

compare the deviation value to a threshold deviation value for the expected range of values for the characteristic of the given object; and

when the deviation value is outside of the threshold deviation value, provide a notification to a driver.

33. The device of claim 32, wherein the processor is further configured to slow the vehicle down if the driver does not take control after the notification is provided.

34. The device of claim 32, wherein the processor is further configured to maneuver the vehicle into a different lane if the driver does not take control after the notification is provided.

35. A device comprising:

memory storing detailed map information describing expected features of the road and characteristics of the expected features;

a processor coupled to the memory, the processor configured to;

receive data from one or more sensors associated with a vehicle;

detect an object and a characteristic for the detected object based on the received data;

determine a deviation value for the detected object based on a comparison of the characteristic and the detailed map information;

compare the deviation value to a threshold deviation value for the expected characteristics of the expected features; and

identify a mismatched area when the deviation value is outside of the threshold deviation value.

36. The device of claim 35, wherein the characteristic includes a position of the detected object and the deviation value is determined by calculating a difference between the position of the detected object and an expected characteristic for position defined in the detailed map information.

37. The device of claim 35, wherein the characteristic includes a shape of the detected object and the deviation value is determined by calculating a difference between the shape of the detected object and an expected characteristic for shape defined in the detailed map information.

38. A non-transitory tangible computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by a processor, cause the processor to perform a method, the method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining a deviation value for the detected object based on a comparison of the characteristic and traffic pattern model information, the traffic pattern model information including an expected range of values for a characteristic of objects in the road;

comparing the deviation value to a threshold deviation value for the expected range of values for the characteristic of the given object; and

when the deviation value is outside of the threshold deviation value, maneuvering, without input from a driver, the vehicle defensively.

39. A non-transitory tangible computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by a processor, cause the processor to perform a method, the method comprising:

receiving data from one or more sensors associated with a vehicle;

detecting an object and a characteristic for the detected object based on the received data;

determining, by a processor, a deviation value for the detected object based on a comparison of the characteristic for the detected object and detailed map information describing expected features of the road and characteristics of the expected features;

comparing the deviation value to a threshold deviation value for the expected characteristics of the expected features; and

identifying a mismatched area when the deviation value is outside of the threshold deviation value.