Object bounding box estimation

• US 10,037,039 B1
• Filed: 05/19/2017
• Issued: 07/31/2018
• Est. Priority Date: 01/30/2012
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Zhu, Jiajun, Haehnel, Dirk
• Primary Examiner(s): Tweel, Jr., John A
• Application Number:
  US15/599,757
• Time in Patent Office: 438 Days
• Field of Search: 340933
• CPC Class Codes: B60K 31/0008 including means for detecting potential obstacles in vehicle path
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/931 of land vehicles
• 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 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/13 Edge detection
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads

Abstract

Aspects of the disclosure relate generally to maneuvering autonomous vehicles. Specifically, the vehicle may use a laser to collect scan data for a section of roadway. The vehicle may access a detailed map including the section of the roadway. A disturbance indicative of an object and including a set of data points data may be identified from the scan data based on the detailed map. The detailed map may also be used to estimate a heading of the disturbance. A bounding box for the disturbance may be estimated using the set of data points as well as the estimated heading. The parameters of the bounding box may then be adjusted in order to increase or maximize the average density of data points of the disturbance along the edges of the bounding box visible to the laser. This adjusted bounding box may then used to maneuver the vehicle.

Claims

1. A method for maneuvering a vehicle, the method comprising:

maneuvering, by one or more processors, the vehicle in an autonomous driving mode according to a control strategy;

receiving, by the one or more processors, a set of data points generated by a sensor of the vehicle, each data point of the set of data points representing a three-dimensional location of a point corresponding to a surface of a particular object in a section of roadway;

receiving, by the one or more processors, information defining a three-dimensional bounding box for the set of data points;

decreasing, by the one or more processors, a volume of the bounding box based on the three-dimensional locations of at least some of the set of data points;

adjusting, by the one or more processors, the control strategy based on the decreased volume; and

maneuvering, by the one or more processors, the vehicle according to the adjusted control strategy.

2. The method of claim 1, wherein decreasing the volume of the bounding box includes ignoring one dimension of the three-dimensional locations of the at least some of the set of data points.

3. The method of claim 2, wherein decreasing the volume of the bounding box by ignoring one dimension of the three-dimensional locations of the at least some of the set of data points includes projecting the three-dimensional locations of the at least some of the set of data points onto a two-dimensional grid.

4. The method of claim 1, wherein the information further includes an orientation of the bounding box, and the method further comprises adjusting the orientation of the bounding box and wherein adjusting the control strategy is further based on the adjusted orientation.

5. The method of claim 1, wherein the information further includes a heading of the bounding box, and the method further comprises adjusting the heading of the bounding box and wherein adjusting the control strategy is further based on the adjusted heading.

6. The method of claim 1, wherein the information further includes a location of the bounding box relative to the roadway, and the method further comprises adjusting the location of the bounding box and wherein adjusting the control strategy is further based on the adjusted location.

7. The method of claim 1, wherein adjusting the control strategy includes maneuvering the vehicle closer to the particular object.

8. The method of claim 1, wherein the particular object is a second vehicle.

9. The method of claim 1, further comprising comparing the set of data points to pre-stored map information in order to identify the particular object as an object in the section of roadway.

10. The method of claim 1, further comprising comparing the set of data points to pre-stored map information in order to determine a heading of the bounding box.

11. The method of claim 10, wherein determining the heading is based on a direction of a traffic lane in which the object is located.

12. The method of claim 11, further comprising generating a two-dimensional intensity map using the at least some of the set of data points, and wherein decreasing the volume of the bounding box is further based on the two-dimensional intensity map.

13. The method of claim 12, further comprising blurring points of the two-dimensional intensity map in order to smooth the two-dimensional intensity map, and decreasing the volume of the bounding box is further based on the smoothed two-dimensional intensity map.

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

maneuver a vehicle in an autonomous driving mode according to a control strategy;

receiving, by the one or more processors, a set of data points generated by a sensor of the vehicle, each data point of the set of data points representing a three-dimensional location of a point corresponding to a surface of a particular object in a section of roadway;

receive information defining a three-dimensional bounding box for the set of data points;

decrease a volume of the bounding box based on the three-dimensional locations of at least some of the set of data points;

adjust the control strategy based on the decreased volume; and

maneuver the vehicle according to the adjusted control strategy.

15. The system of claim 14, wherein decreasing the volume of the bounding box includes ignoring one dimension of the three-dimensional locations of the at least some of the set of data points.

16. The system of claim 14, wherein the one or more processors are further configured to compare the set of data points to pre-stored map information in order to determine a heading of the bounding box.

17. The system of claim 16, wherein determining the heading is based on a direction of a traffic lane in which the particular object is located.

18. The system of claim 17, wherein the one or more processors are further configured to generate a two-dimensional intensity map using the at least some of the set of data points, and wherein decreasing the volume of the bounding box is further based on the two-dimensional intensity map.

19. The system of claim 18, wherein the one or more processors are further configured to blur points of the two-dimensional intensity map in order to smooth the two-dimensional intensity map, and decreasing the volume of the bounding box is further based on the smoothed two-dimensional intensity map.

20. The system of claim 14, further comprising the vehicle.