Avoiding blind spots of other vehicles

• US 8,874,267 B1
• Filed: 01/09/2014
• Issued: 10/28/2014
• Est. Priority Date: 06/20/2012
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dolgov, Dmitri A., Urmson, Christopher Paul
• Primary Examiner(s): Tran, Khoi
• Assistant Examiner(s): Nguyen, Bao Long T
• Application Number:
  US14/150,901
• Time in Patent Office: 292 Days
• US Class Current: 700/255
• 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 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/402 Type
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2554/806 Relative heading
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/095 Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 30/16 Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: B60W 60/00272 relying on extrapolation of current movement
• CPC Class Codes: G01C 21/26 specially adapted for navigation in a road network
• CPC Class Codes: G05D 1/0061 for transition from automatic pilot to manual pilot and vice versa
• 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: G05D 1/0214 in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G08G 1/16 : Anti-collision systems road vehicle drive control systems for predicting or avoiding probable or impending collision otherwise than by control of a particular sub-unit B60W30/08
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Aspects of the disclosure relate generally to detecting and avoiding blind spots of other vehicles when maneuvering an autonomous vehicle. Blind spots may include both areas adjacent to another vehicle in which the driver of that vehicle would be unable to identify another object as well as areas that a second driver in a second vehicle may be uncomfortable driving. In one example, a computer of the autonomous vehicle may identify objects that may be relevant for blind spot detecting and may determine the blind spots for these other vehicles. The computer may predict the future locations of the autonomous vehicle and the identified vehicles to determine whether the autonomous vehicle would drive in any of the determined blind spots. If so, the autonomous driving system may adjust its speed to avoid or limit the autonomous vehicle'"'"'s time in any of the blind spots.

Claims

1. A method comprising:

detecting, by the one or more processors, objects in a roadway based on the sensor data collected along the roadway by sensors of a vehicle driving in an autonomous driving mode;

identifying, by the one or more processors, from the detected objects, a particular object of the objects in the roadway that are located in a lane adjacent to a lane of the vehicle for blind spot detection;

determining, by the one or more processors, a blind spot area that defines a space in the lane of the vehicle that is adjacent to the particular object;

predicting, by the one or more processors, future locations of the particular object;

determining, by the one or more processors, whether the predicted future locations indicate that the vehicle will drive into the determined blind spot area;

when the predicted future locations indicate that the vehicle will drive into the determined blind spot area, determining, by one or more processors, a controlling factor for the blind spot area including a restriction on an amount of time spent by the vehicle in the determined blind spot area when the vehicle is in the determined blind spot area; and

maneuvering, by the one or more processors, the vehicle based on the controlling factor.

2. The method of claim 1, further comprising, for the particular object, determining a set of estimated characteristics, including one or more of the size, shape, location, and type of object.

3. The method of claim 2, wherein identifying the particular object is based on the set of estimated characteristics of the particular object.

4. The method of claim 1, wherein determining the blind spot area for the particular object is further based on a shape or a size of the particular object.

5. The method of claim 1, further comprising:

inputting the restriction into a speed algorithm to determine speed control for the vehicle; and

wherein maneuvering the vehicle is further based on the determined speed control.

6. The method of claim 1, wherein determining the future locations is based on a threshold period of time, and the method further comprises:

after the threshold period of time, repredicting, by the one or more processors, future locations of the particular object; and

determining, by the one or more processors, whether the repredicted future locations indicate that the vehicle would drive into the determined blind spot areas.

7. The method of claim 6, further comprising:

when the repredicted future locations indicate that the vehicle would drive into the determined blind spot areas, determining a second controlling factor for the blind spot area; and

maneuvering the vehicle based on the second controlling factor.

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

detect objects in a roadway based on the sensor data collected along the roadway by sensors of a vehicle driving in an autonomous driving mode;

identify a particular object of the objects in the roadway that are located in a lane adjacent to a lane of the vehicle for blind spot detection;

determine a blind spot area that defines a space in the lane of the vehicle that is adjacent to the particular object;

predict future locations of the particular object;

determine whether the predicted future locations indicate that the vehicle will drive into the determined blind spot area;

when the predicted future locations indicate that the vehicle would will drive into the determined blind spot areas, determine a controlling factor for the blind spot area including a restriction on an amount of time spent by the vehicle in the determined blind spot area when the vehicle is in the determined blind spot area; and

maneuver the vehicle based on the controlling factor.

9. The system of claim 8, wherein the one or more processors are further configured to, determine for the particular object a set of estimated characteristics, including one or more of the size, shape, location, and type of object.

10. The system of claim 9, wherein the one or more processors are further configured to identify the particular object based on the set of estimated characteristics of the particular object.

11. The system of claim 9, wherein the one or more processors are further configured to determine the blind spot area for the particular object further based on a shape or a size of the particular object.

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

input the restriction into a speed algorithm to determine speed control for the vehicle; and

maneuver the vehicle further based on the determined speed control.

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

determine the future locations based on a threshold period of time, and the method further comprises;

after the threshold period of time, repredict future locations of the particular object; and

determine whether the repredicted future locations indicate that the vehicle would drive into the determined blind spot areas.

14. The system of claim 13, wherein the one or more processors are further configured to:

when the repredicted future locations indicate that the vehicle would drive into the determined blind spot areas, determine a second controlling factor for the blind spot area; and

maneuver the vehicle based on the second controlling factor.

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

detecting objects in a roadway based on the sensor data collected along the roadway by sensors of a vehicle driving in an autonomous driving mode;

identifying, from the detected objects, a particular object of the objects in the roadway that are located in a lane adjacent to a lane of the vehicle for blind spot detection;

determining a blind spot area that defines a space in the lane of the vehicle that is adjacent to the particular object;

predicting future locations of the particular object;

determining whether the predicted future locations indicate that the vehicle will drive into the determined blind spot area;

when the predicted future locations indicate that the vehicle will drive into the determined blind spot area, determining a controlling factor for the blind spot area including a restriction on an amount of time spent by the vehicle in the determined blind spot when the vehicle is in the determined blind spot area; and

maneuvering the vehicle based on the controlling factor.

16. The medium of claim 15, wherein the method further comprises, for the particular object, determining a set of estimated characteristics, including one or more of the size, shape, location, and type of object.

17. The medium of claim 16, wherein the method further comprises identifying the particular object based on the set of estimated characteristics of the particular object.

18. The medium of claim 15, wherein the method further comprises determining the blind spot area for the particular object further based on a shape or a size of the particular object.

19. The medium of claim 15, wherein the method further comprises:

inputting the restriction into a speed algorithm to determine speed control for the vehicle; and

wherein maneuvering the vehicle is further based on the determined speed control.

20. The medium of claim 15, wherein the method further comprises:

determining the future locations based on a threshold period of time;

after the threshold period of time, repredicting, by the one or more processors, future locations of the particular object; and

determining, by the one or more processors, whether the repredicted future locations indicate that the vehicle would drive into the determined blind spot areas.