Avoiding blind spots of other vehicles

• US 9,180,882 B1
• Filed: 06/20/2012
• Issued: 11/10/2015
• Est. Priority Date: 06/20/2012
• Status: Expired due to Fees

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Dolgov, Dmitri A., Urmson, Christopher Paul
• Primary Examiner(s): Nguyen, Bao Long T
• Application Number:
  US13/528,122
• Time in Patent Office: 1,238 Days
• US Class Current: 1/1
• 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 for maneuvering a first vehicle, the method comprising:

receiving, by one or more processors, sensor data collected along a roadway;

detecting, by the one or more processors, objects in the roadway based on the sensor data;

identifying, by the one or more processors, from the detected objects, a set of objects that are relevant for blind spot detection such that each object of the set of identified objects is another vehicle traveling in a respective lane adjacent to a first lane in which the first vehicle is currently traveling and every object of the set of identified objects is currently traveling in a same direction of traffic as the first vehicle;

for each identified object of the set of identified objects, determining, by the one or more processors, a blind spot area that defines a space in the first lane that is adjacent to a particular identified object of the set of identified objects, the blind spot area being determined by the one or more processors executing an algorithm that determines the blind spot area based on at least one of an estimated speed of the particular identified object, or a heading of the particular identified object;

predicting, by the one or more processors, future locations of the set of identified objects and the first vehicle;

determining, by the one or more processors, whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than a threshold period of time, wherein the threshold period of time is greater than zero;

when the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than the threshold period of time, determining, by the one or more processors, a controlling factor for each determined blind spot area; and

maneuvering, by the one or more processors, the first vehicle based on the controlling factor determined for each determined blind spot area.

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

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

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

5. The method of claim 1, wherein determining whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spots for more than the threshold period of time includes

determining an amount of time that the first vehicle would drive in a given one of any of the determined blind spot areas using the predicted future locations;

and

comparing the amount of time to the threshold period of time.

6. The method of claim 1, wherein each controlling factor include a set of constraints and the method further comprises inputting the set of constraints of each controlling factor into a speed algorithm to determine speed control for the first vehicle.

7. The method of claim 1, wherein maneuvering the first vehicle includes maneuvering the vehicle into one of the determined blind spot areas.

8. The device of claim 1, wherein each controlling factor includes a set of constraints, and the processor is further configured to incorporate the set of constraints of each controlling factor into a speed algorithm in order to determine speed control for the first vehicle.

9. The method of claim 8, wherein the processor is further configured to generate the instructions for maneuvering the first vehicle based on the determined speed control.

10. A device comprising:

one or more processors configured to;

receive sensor data collected as a first vehicle is maneuvered along a roadway;

detect objects in the roadway based on the sensor data;

identify, from the detected objects, a set of objects that are relevant for blind spot detection such that each object of the set of identified objects is another vehicle traveling in a respective lane adjacent to a first lane in which the first vehicle is currently traveling and every object of the set of identified objects is currently traveling in a same direction of traffic as the first vehicle;

for each identified object of the set of identified objects, determine a blind spot area that defines a space in the first lane that is adjacent to a particular identified object of the set of identified objects, the blind spot area being determined by the one or more processors executing an algorithm that determines the blind spot area based on at least one of an estimated speed of the particular identified object, or a heading of the particular identified object;

predict future locations of the set of identified objects and the first vehicle;

determine whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than a threshold period of time, wherein the threshold period of time is greater than zero;

when the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than the threshold period of time, determine a controlling factor for each determined blind spot area; and

generate instructions for maneuvering the first vehicle based on the controlling factor determined for each determined blind spot area.

11. The device of claim 10, wherein the processor is further configured to, for each determined object, determine a set of estimated characteristics, including one or more of a size, shape, location, and type of object.

12. The device of claim 11, wherein the processor is further configured to identify the set of objects based on the estimated characteristics of each determined object.

13. The device of claim 10, wherein the processor is further configured to determine the blind spot area for a particular object further based on a shape or a size of the particular object.

14. The device of claim 10, wherein the processor is further configured to determine whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than the threshold period of time includes:

determining an amount of time that the first vehicle would drive in a given one of any of the determined blind spot areas using the predicted future locations; and

comparing the amount of time to the threshold period of time.

15. 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 for maneuvering a first vehicle, the method comprising:

receiving sensor data collected along a roadway;

detecting objects in the roadway based on the sensor data;

identifying, from the detected objects, a set of objects that are relevant for blind spot detection such that each object of the set of identified objects is another vehicle traveling in a respective lane adjacent to a first lane in which the first vehicle is currently traveling and every object of the set of identified objects is currently traveling in a same direction of traffic as the first vehicle;

for each identified object of the set of identified objects, determining a blind spot area that defines a space in the first lane that is adjacent to a particular identified object of the set of identified objects, the blind spot area being determined by the processor executing an algorithm that determines the blind spot area based on at least one of an estimated speed of the particular identified object, or a heading of the particular identified object;

predicting future locations of the set of identified objects and the vehicle;

determining whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than a threshold period of time, wherein the threshold period of time is greater than zero;

when the predicted future locations indicate that the vehicle would drive in any of the determined blind spot areas for more than the threshold period of time, determining a controlling factor for each determined blind spot area; and

maneuvering the vehicle based on the controlling factor determined for each determined blind spot area.

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

17. The medium of claim 16, wherein identifying the set of objects is based on the estimated characteristics of each determined object.

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

19. The medium of claim 15, wherein determining whether the predicted future locations indicate that the first vehicle would drive in any of the determined blind spot areas for more than the threshold period of time includes:

determining an amount of time that the first vehicle would drive in a given one of any of the determined blind spot areas using the predicted future locations; and

comparing the amount of time to the threshold period of time.

20. The medium of claim 15, wherein each controlling factor include a set of constraints, and the method further comprises inputting the set of constraints of each controlling factor into a speed algorithm to determine speed control for the first vehicle.