Predictive reasoning for controlling speed of a vehicle

• US 9,381,917 B1
• Filed: 08/17/2015
• Issued: 07/05/2016
• Est. Priority Date: 05/03/2013
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dolgov, Dmitri, Ferguson, Dave
• Primary Examiner(s): Shaawat, Mussa A
• Assistant Examiner(s): Ingram, Thomas
• Application Number:
  US14/827,578
• Publication Number:
  US 20160214607A1
• Time in Patent Office: 323 Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2554/801 Lateral distance
• CPC Class Codes: B60W 2554/802 Longitudinal distance
• CPC Class Codes: B60W 2554/803 Relative lateral speed
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 2554/805 Azimuth angle
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2754/30 Longitudinal distance
• CPC Class Codes: B60W 30/143 Speed control B60W30/16 takes precedence
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 60/0016 of the vehicle or its occupants
• 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 17/89 for mapping or imaging
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9325 for inter-vehicle distance regulation, e.g. navigating in platoons
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: G08G 1/22 Platooning, i.e. convoy of communicating vehicles

Abstract

Methods and systems for predictive reasoning for controlling speed of a vehicle are described. A computing device may be configured to identify a first and second vehicle travelling ahead of an autonomous vehicle and in a same lane as the autonomous vehicle. The computing device may also be configured to determine a first buffer distance behind the first vehicle at which the autonomous vehicle will substantially reach a speed of the first vehicle and a second buffer distance behind the second vehicle at which the first vehicle will substantially reach a speed of the second vehicle. The computing device may further be configured to determine a distance at which to adjust a speed of the autonomous vehicle based on the first and second buffer distances and the speed of the autonomous vehicle, and then provide instructions to adjust the speed of the autonomous vehicle based on the distance.

Claims

1. A method comprising:

identifying, by at least one processor of a computing device, based on data received from at least one sensor communicatively linked to the computing device, a vehicle travelling ahead of an autonomous vehicle in substantially a same lane as the autonomous vehicle;

the at least one processor identifying, based on the data, a traffic control object ahead of the vehicle;

the at least one processor determining a state of the traffic control object ahead of the vehicle;

the at least one processor determining a buffer distance, the buffer distance being a minimal distance behind the vehicle at which the autonomous vehicle is predicted to substantially reach a speed of the vehicle;

the at least one processor determining a distance at which to adjust a speed of the autonomous vehicle based on the buffer distance, the state of the traffic control object, and the speed of the autonomous vehicle; and

the at least one processor controlling the autonomous vehicle to adjust the speed of the autonomous vehicle based on the distance.

2. The method of claim 1, wherein determining the state of the traffic control object comprises determining a current state of the traffic control object, the current state of the traffic control object being a state of the traffic control object at a time the traffic control object is identified, and

wherein determining the distance based on the state of the traffic control object comprises determining the distance based on the current state of the traffic control object.

3. The method of claim 1, wherein determining the state of the traffic control object comprises (i) determining a current state of the traffic control object, the current state of the traffic control object being a state of the traffic control object at a time the traffic control object is identified, and (ii) based on the current state of the traffic control object, determining a predicted state of the traffic control object to which the traffic control object is predicted to change a predetermined period of time after the time the traffic control object is identified, and

wherein determining the distance based on the state of the traffic control object comprises determining the distance based on the predicted state of the traffic control object.

4. The method of claim 1, wherein the vehicle and the traffic control object are within a longitudinal distance threshold from the autonomous vehicle and within a lateral distance threshold from the autonomous vehicle.

5. The method of claim 1, wherein the speed of the vehicle includes one or more of a longitudinal speed of the vehicle and a lateral speed of the vehicle, and

wherein the distance at which to adjust a speed of the autonomous vehicle is further based on one or more of:

an acceleration of the vehicle, a deceleration of the vehicle, whether the vehicle is moving or stationary, a direction of motion of the vehicle, a size of the vehicle, a weight of the vehicle, a position of the vehicle on a road of travel, and a type of the vehicle.

6. The method of claim 1, wherein the distance at which to adjust the speed of the autonomous vehicle is further based on one or more of:

an acceleration of the autonomous vehicle, a deceleration of the autonomous vehicle, a direction of motion of the autonomous vehicle, a size of the autonomous vehicle, a position of the autonomous vehicle on a road of travel, and a type of the autonomous vehicle.

7. The method of claim 1, wherein the vehicle is a first vehicle, and wherein the buffer distance is a first buffer distance, the method further comprising:

the at least one processor identifying, based on the data, a second vehicle ahead of the first vehicle and between the first vehicle and the traffic control object, the second vehicle travelling in substantially the same lane as the autonomous vehicle and the first vehicle; and

the at least one processor determining a second buffer distance, the second buffer distance being a minimal distance behind the second vehicle at which the first vehicle is predicted to substantially reach a speed of the second vehicle,

wherein the distance at which to adjust the speed of the autonomous vehicle is further based on the second buffer distance.

8. The method of claim 7, wherein the first and second buffer distances are further based on lengths of the first and second vehicles and a predetermined minimal gap between the first and second vehicles at zero speed.

9. A non-transitory computer readable medium having stored thereon instructions that, upon execution by a computing device, cause the computing device to perform operations comprising:

identifying, based on data received from at least one sensor communicatively linked to the computing device, a vehicle travelling ahead of an autonomous vehicle in substantially a same lane as the autonomous vehicle;

identifying, based on the data, a traffic control object ahead of the vehicle;

determining a state of the traffic control object ahead of the vehicle;

determining a buffer distance, the buffer distance being a minimal distance behind the vehicle at which the autonomous vehicle is predicted to substantially reach a speed of the vehicle;

determining a distance at which to adjust a speed of the autonomous vehicle based on the buffer distance, the state of the traffic control object, and the speed of the autonomous vehicle; and

controlling the autonomous vehicle to adjust the speed of the autonomous vehicle based on the distance.

10. The non-transitory computer readable medium of claim 9, wherein the traffic control object is a traffic light configured to operate in at least three distinct states, the at least three distinct states including a red light state, a yellow light state, and a green light state.

11. The non-transitory computer readable medium of claim 9, wherein the traffic control object is a stop sign.

12. The non-transitory computer readable medium of claim 9, wherein the traffic control object is a traffic cone, a road surface marking, or a road boundary barrier.

13. The non-transitory computer readable medium of claim 9, wherein the at least one sensor includes an image-capture device coupled to the autonomous vehicle, the operations further comprising:

receiving, from the image-capture device, one or more images,

wherein the data includes the one or more images,

wherein identifying the vehicle based on the data comprises identifying the vehicle in the one or more images,

wherein identifying the traffic control object based on the data comprises identifying the traffic control object in the one or more images, and

wherein determining the state of the traffic control object comprises determining the state of the traffic control object based at least in part on the one or more images.

14. A system comprising:

at least one sensor;

at least one processor; and

a memory having stored thereon instructions that, upon execution by the at least one processor, cause the system to perform operations comprising;

identifying, based on data received from the at least one sensor, a vehicle travelling ahead of an autonomous vehicle in substantially a same lane as the autonomous vehicle;

identifying, based on the data, a traffic control object ahead of the vehicle;

determining a state of the traffic control object ahead of the vehicle;

determining a buffer distance, the buffer distance being a minimal distance behind the vehicle at which the autonomous vehicle is predicted to substantially reach a speed of the vehicle;

determining a distance at which to adjust a speed of the autonomous vehicle based on the buffer distance, the state of the traffic control object, and the speed of the autonomous vehicle; and

controlling the autonomous vehicle to adjust the speed of the autonomous vehicle based on the distance.

15. The system of claim 14, wherein determining the distance at which to adjust the speed of the autonomous vehicle comprises reducing a distance from the autonomous vehicle to the traffic control object by the buffer distance.

16. The system of claim 14, wherein the vehicle is a first vehicle, and wherein the buffer distance is a first buffer distance, the operations further comprising:

identifying, based on the data, a second vehicle ahead of the first vehicle and between the first vehicle and the traffic control object, the second vehicle travelling in substantially the same lane as the autonomous vehicle and the first vehicle; and

determining a second buffer distance, the second buffer distance being a minimal distance behind the second vehicle at which the first vehicle is predicted to substantially reach a speed of the second vehicle,

wherein the distance at which to adjust the speed of the autonomous vehicle is further based on the second buffer distance.

17. The system of claim 14, wherein the traffic control object is a traffic light configured to operate in at least three distinct states, the at least three distinct states including a red light state, a yellow light state, and a green light state, and

wherein controlling the autonomous vehicle to adjust the speed of the autonomous vehicle comprises controlling the autonomous vehicle to adjust the speed of the autonomous vehicle prior to determining a change of the state of the traffic control object.

18. The system of claim 14, wherein determining the state of the traffic control object comprises (i) determining a current state of the traffic control object, the current state of the traffic control object being a state of the traffic control object at a time the traffic control object is identified, and (ii) based on the current state of the traffic control object, determining a predicted state of the traffic control object to which the traffic control object is predicted to change a predetermined period of time after the time the traffic control object is identified, the operations further comprising:

based on one or both of the current state of the traffic control object and the predicted state of the traffic control object, estimating a predicted change in the speed of the vehicle,

wherein the distance is further based on the predicted change in the speed of the vehicle.

19. The system of claim 18, wherein controlling the autonomous vehicle to adjust the speed of the autonomous vehicle comprises controlling the autonomous vehicle to adjust the speed of the autonomous vehicle prior to estimating the predicted change of the speed of the vehicle.

20. The system of claim 14, wherein the at least one sensor includes:

a light detection and ranging (LIDAR) device coupled to the autonomous vehicle, wherein the LIDAR device is configured to provide LIDAR-based information comprising a three-dimensional (3D) point cloud that includes a set of points based on light emitted from the LIDAR device and reflected from the vehicle and the traffic control object;

a camera coupled to the autonomous vehicle, wherein the camera is configured to provide one or more images of the vehicle and the traffic control object; and

a radio detection and ranging (RADAR) device coupled to the autonomous vehicle, wherein the RADAR device is configured to provide RADAR-based information relating to at least one of;

an acceleration of the vehicle, a deceleration of the vehicle, whether the vehicle is moving or stationary, a direction of motion of the vehicle, a size of the vehicle, a weight of the vehicle, a position of the vehicle on a road of travel, and a type of the vehicle,

wherein the data includes the LIDAR-based information, the one or more images, and the RADAR-based information.