Object detection for an autonomous vehicle

• US 9,672,446 B1
• Filed: 05/06/2016
• Issued: 06/06/2017
• Est. Priority Date: 05/06/2016
• Status: Active Grant

• Current Assignee: Aurora Operations Incorporated (Aurora Innovation, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Vallespi-Gonzalez, Carlos
• Primary Examiner(s): Badii, Behrang
• Assistant Examiner(s): GREENE, DANIEL LAWSON
• Application Number:
  US15/148,970
• Time in Patent Office: 396 Days
• CPC Class Codes: B60R 11/04 Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
• 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 1/0251 extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G06F 18/24 Classification techniques
• 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
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: H04N 13/204 using stereoscopic image cameras stereoscopic photography G03B35/00
• CPC Class Codes: H04N 13/239 using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence
• CPC Class Codes: H04N 2013/0081 Depth or disparity estimation from stereoscopic image signals

Abstract

An object detection system for an autonomous vehicle processes sensor data, including one or more images, obtained for a road segment on which the autonomous vehicle is being driven. The object detection system compares the images to three-dimensional (3D) environment data for the road segment to determine pixels in the images that correspond to objects not previously identified in the 3D environment data. The object detection system then analyzes the pixels to classify the objects not previously identified in the 3D environment data.

Claims

1. An object detection system for an autonomous vehicle (AV) comprising:

a memory to store an instruction set; and

one or more processors to execute instructions from the instruction set to;

process sensor data obtained for a road segment on which the autonomous vehicle is being driven, wherein the processed sensor data includes one or more images;

compare the one or more images to three-dimensional (3D) environment data for the road segment to determine a plurality of pixels in the one or more images that correspond to objects not previously identified in the 3D environment data; and

analyze the plurality of pixels to classify the objects not previously identified in the 3D environment data.

2. The object detection system of claim 1, including further instructions that the one or more processors execute to:

identify a subset of sensor data from non-image sources corresponding to the plurality of pixels in the one or more images; and

classify the objects not previously identified in the 3D environment data based on analyzing the subset of sensor data.

3. The object detection system of claim 1, including further instructions that the one or more processors execute to:

adjust operation of the autonomous vehicle based at least on a classification of the objects.

4. The object detection system of claim 1, wherein the one or more images include disparity data calculated from a pair of images taken from a stereoscopic camera.

5. The object detection system of claim 1, wherein the one or more images include optical flow vectors calculated from a first image of the road segment and a second image of the road segment taken after the first image.

6. The object detection system of claim 1, wherein the objects are classified into classes which include pedestrians, bicycles, and other vehicles.

7. A method for object detection, the method being implemented by one or more processors of an autonomous vehicle and comprising:

processing sensor data obtained for a road segment on which the autonomous vehicle is being driven, wherein the processed sensor data includes one or more images;

comparing the one or more images to three-dimensional (3D) environment data for the road segment to determine a plurality of pixels in the one or more images that correspond to objects not previously identified in the 3D environment data; and

analyzing the plurality of pixels to classify the objects not previously identified in the 3D environment data.

8. The method of claim 7, further comprising:

identifying a subset of sensor data from non-image sources corresponding to the plurality of pixels in the one or more images; and

classifying the objects not previously identified in the 3D environment data based on analyzing the subset of sensor data.

9. The method of claim 7, further comprising:

adjusting operation of the autonomous vehicle based at least on a classification of the objects.

10. The method of claim 7, wherein the one or more images include disparity data calculated from a pair of images taken from a stereoscopic camera.

11. The method of claim 7, wherein the one or more images include optical flow vectors calculated from a first image of the road segment and a second image of the road segment taken after the first image.

12. The method of claim 7, wherein the objects are classified into classes which include pedestrians, bicycles, and other vehicles.

13. A vehicle comprising:

one or more sensors to obtain sensor data from an environment around the vehicle;

a memory to store an instruction set; and

one or more processors to execute instructions from the instruction set to;

process sensor data obtained for a road segment on which the vehicle is being driven, wherein the processed sensor data includes one or more images;

compare the one or more images to three-dimensional (3D) environment data for the road segment to determine a plurality of pixels in the one or more images that correspond to objects not previously identified in the 3D environment data; and

analyze the plurality of pixels to classify the objects not previously identified in the 3D environment data.

14. The vehicle of claim 13, including further instructions that the one or more processors execute to:

identify a subset of sensor data from non-image sources corresponding to the plurality of pixels in the one or more images; and

classify the objects not previously identified in the 3D environment data based on analyzing the subset of sensor data.

15. The vehicle of claim 13, including further instructions that the one or more processors execute to:

adjust operation of the vehicle based at least on a classification of the objects.

16. The vehicle of claim 13, wherein the one or more images include disparity data calculated from a pair of images taken from a stereoscopic camera.

17. The vehicle of claim 13, wherein the one or more images include optical flow vectors calculated from a first image of the road segment and a second image of the road segment taken after the first image.

18. The vehicle of claim 13, wherein the objects are classified into classes which include pedestrians, bicycles, and other vehicles.