Determining when to drive autonomously

• US 8,954,217 B1
• Filed: 03/20/2014
• Issued: 02/10/2015
• 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, Murveit, Hyman Jack, Urmson, Christopher Paul, Dolgov, Dmitri A., Nemec, Philip
• Primary Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US14/220,653
• Time in Patent Office: 327 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 2420/52 Radar, 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, by one or more processors, data collected by one or more sensors associated with a vehicle;

detecting, by the one or more processors, an object and a characteristic of the detected object based on the received data;

determining, by the one or more processors, a deviation value for the characteristic of the detected object based on a comparison of the characteristic of the detected object to a characteristic of an object identified in detailed map information;

selecting a threshold deviation value based on the object identified in the detailed map information; and

controlling, by the one or more processors, the vehicle based on whether the deviation value satisfies the selected threshold deviation value.

2. The method of claim 1, wherein selecting the threshold deviation value is further based on a type of the object in the detailed map information.

3. The method of claim 1, wherein selecting the threshold deviation value is further based on a type of the characteristic of the detected first object.

4. 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 a characteristic of a second object identified in the detailed map information;

selecting a second threshold deviation value based on the comparison detailed map information, wherein the selected second threshold deviation value is different from the selected threshold deviation value;

controlling the vehicle based on whether the second deviation value satisfies the selected second threshold deviation value.

5. 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 characteristic of the object identified in the detailed map information;

controlling the vehicle based on whether the second deviation value satisfies the selected threshold deviation value.

6. The method of claim 1, wherein the characteristic is a position of the detected object, and the method further comprises:

detecting a second characteristic for the detected object based on the received data, wherein the second characteristic indicates a shape of the detected object;

determining a second deviation value for the second detected object based on a comparison of the second characteristic for the detected object to a second characteristic of the second object identified in the detailed map information, wherein the second characteristic of the second object identified in the detailed map information indicates an expected shape of the second object;

selecting a second threshold deviation value based on the comparison of the second characteristic for the detected object to the second characteristic of the second object identified in the detailed map information, wherein the selected second threshold deviation value is different from the selected threshold deviation value;

controlling the vehicle based on whether the second deviation value satisfies the selected second threshold deviation value.

7. The method of claim 1, further comprising providing a notification to a driver based on whether the deviation value satisfies the selected threshold deviation value.

8. A system, comprising one or more processors configured to:

receive data collected by one or more sensors associated with a vehicle;

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

determine a deviation value for the characteristic of the detected object based on a comparison of the characteristic of the detected first object to a characteristic of an object identified in detailed map information;

select a threshold deviation value based on the object identified in the detailed map information; and

control the vehicle based on whether the deviation value satisfies the selected threshold deviation value.

9. The system of claim 8, wherein selecting the threshold deviation value is further based on a type of the object in the detailed map information.

10. The system of claim 8, wherein selecting the threshold deviation value is further based on a type of the characteristic of the detected first object.

11. The system of claim 8, wherein the one or more processors are further configured to:

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

determine a second deviation value for the second detected object based on a comparison of the second characteristic for the second detected object to a characteristic of a second object identified in the detailed map information;

select a second threshold deviation value based on the comparison detailed map information, wherein the selected second threshold deviation value is different from the selected threshold deviation value;

control the vehicle based on whether the second deviation value satisfies the selected second threshold deviation value.

12. The system of claim 8, wherein the one or more processors are further configured to:

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

determine a second deviation value for the second detected object based on a comparison of the second characteristic for the second detected object to the characteristic of the object identified in the detailed map information;

control the vehicle based on whether the second deviation value satisfies the selected threshold deviation value.

13. The system of claim 8, wherein the characteristic is a position of the detected object, and the one or more processors are further configured to:

detect a second characteristic for the detected object based on the received data, wherein the second characteristic indicates a shape of the detected object;

determine a second deviation value for the second detected object based on a comparison of the second characteristic for the detected object to a second characteristic of the second object identified in the detailed map information, wherein the second characteristic of the second object identified in the detailed map information indicates an expected shape of the second object;

select a second threshold deviation value based on the comparison of the second characteristic for the detected object to the second characteristic of the second object identified in the detailed map information, wherein the selected second threshold deviation value is different from the selected threshold deviation value;

control the vehicle based on whether the second deviation value satisfies the selected second threshold deviation value.

14. The system of claim 8, wherein the one or more processors are further configured to provide a notification to a driver based on whether the deviation value satisfies the selected threshold deviation value.

15. The system of claim 8, further comprising the vehicle, and wherein the one or more processors are associated with the vehicle.

16. A non-transitory, computer-readable storage medium on which computer readable instructions of a program are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:

receiving data collected by one or more sensors associated with a vehicle;

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

determining a deviation value for the characteristic of the detected object based on a comparison of the characteristic of the detected first object to a characteristic of an object identified in detailed map information;

selecting a threshold deviation value based on the object identified in the detailed map information; and

controlling the vehicle based on whether the deviation value satisfies the selected threshold deviation value.

17. The medium of claim 16, wherein selecting the threshold deviation value is further based on a type of the object in the detailed map information.

18. The medium of claim 16, wherein selecting the threshold deviation value is further based on a type of the characteristic of the detected first object.

19. The medium of claim 16, wherein the method further comprises:

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 a characteristic of a second object identified in the detailed map information;

selecting a second threshold deviation value based on the comparison detailed map information, wherein the selected second threshold deviation value is different from the selected threshold deviation value;

controlling the vehicle based on whether the second deviation value satisfies the selected second threshold deviation value.

20. The medium of claim 16, wherein the method further comprises:

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 characteristic of the object identified in the detailed map information;

controlling the vehicle based on whether the second deviation value satisfies the selected threshold deviation value.