System and method for evaluating the perception system of an autonomous vehicle

• US 9,122,948 B1
• Filed: 10/05/2011
• Issued: 09/01/2015
• Est. Priority Date: 10/05/2010
• Status: Expired due to Fees

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Zhu, Jiajun, Urmson, Christopher Paul, Haehnel, Dirk, Fairfield, Nathaniel, Smith, Russell Leigh
• Primary Examiner(s): Bella, Matthew
• Assistant Examiner(s): YANG, WEI WEN
• Application Number:
  US13/200,958
• Time in Patent Office: 1,427 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: B60R 1/23 with a predetermined field of view
• CPC Class Codes: B60R 2300/30 characterised by the type of image processing
• CPC Class Codes: B60T 17/18 Safety devices; Monitoring
• CPC Class Codes: B60T 17/221 Procedure or apparatus for checking or keeping in a correct functioning condition of brake systems hydraulic pressure systems in general F15B19/00, F15B21/04; testing structures or apparatus G01M
• CPC Class Codes: B60T 2201/022 Collision avoidance systems
• CPC Class Codes: B60T 2210/32 Vehicle surroundings
• CPC Class Codes: B60T 2270/406 Test-mode; Self-diagnosis
• CPC Class Codes: B60T 7/22 initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle , or by means of contactless obstacle detectors mounted on the vehicle
• CPC Class Codes: B60T 8/00 Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force by changing number of effective brake cylinders in power brake systems B60T17/10
• CPC Class Codes: B60T 8/17 Using electrical or electronic regulation means to control braking detecting or indicating faulty operation B60T8/885
• CPC Class Codes: B60T 8/885 using electrical circuitry
• CPC Class Codes: B60W 2050/0292 Fail-safe or redundant systems, e.g. limp-home or backup systems
• 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 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• 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 30/186 excessive wear or burn out of friction elements, e.g. clutches
• CPC Class Codes: B60W 40/06 : Road conditions
• CPC Class Codes: B60W 50/14 : Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B62D 6/00 : Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits means for initiating a change in direction B62D1/00; steering valves B62D5/06; combined with means for inclining the vehicle body or wheels on bends B62D9/00
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• 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 17/86 : Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/931 : of land vehicles
• CPC Class Codes: G05B 2219/2637 : Vehicle, car, auto, wheelchair
• CPC Class Codes: G05D 1/0055 : with safety arrangements
• 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 1/021 : specially adapted to land vehicles
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• 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: G05D 1/024 : in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0257 : using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0274 : using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0276 : using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06Q 10/02 : Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 : Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10028 : Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30236 : Traffic on road, railway or crossing
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/223 : using block-matching
• CPC Class Codes: G06T 7/231 : using full search
• CPC Class Codes: G06T 7/521 : from laser ranging, e.g. using interferometry; from the projection of structured light
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• 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
• CPC Class Codes: G06V 20/588 : Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G06V 20/64 : Three-dimensional objects
• CPC Class Codes: G07C 9/00563 : using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns

Abstract

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Claims

1. An apparatus for optimizing object detection performed by an autonomous vehicle, the apparatus comprising:

a memory; and

a processor in communication with the memory, the processor operative to;

receive a first plurality of images captured by an autonomous vehicle, wherein a first image in the first plurality of images includes at least one applied object label applied by the autonomous vehicle;

display the first image from the first plurality of images, wherein the first image comprises at least one object;

receive an object label for the at least one object displayed in the first image to obtain a received object label in a first image in a second plurality of images, wherein the second plurality of images corresponds to the first plurality of images;

compare the at least one received object label with at least one object label applied by the autonomous vehicle to the object in the first image in the first plurality of images;

determine whether the at least one received object label corresponds to the at least one object label applied by the autonomous vehicle,

compare a number of object labels applied by the autonomous vehicle in the first image in the first plurality of images with a number of object labels received in the first image in the second plurality of images;

determining whether the autonomous vehicle missed a predetermined number of the object labels in the first image in the first plurality of images based on the comparison of the number of object labels applied by the autonomous vehicle in the first image in the first plurality of images and the number of object labels received in the first image in the second plurality of images; and

optimize a plurality of object detection parameters of at least one of a plurality of sensors when an indication of a correspondence between the received object label and the object label applied by the autonomous vehicle is below a predetermined threshold and the autonomous vehicle missed the predetermined number of the object labels in the first image of the first plurality of images;

wherein the object label applied by the autonomous vehicle is defined by at least one object label parameter that corresponds to a type of sensor that captured the first image.

2. The apparatus of claim 1, wherein the first plurality of images comprise a first plurality of images captured by a first sensor of a first sensor type and a second plurality of images captured by a second sensor of a second sensor type.

3. The apparatus of claim 2, wherein the first sensor comprises a camera and the second sensor comprises a laser.

4. The apparatus of claim 2, wherein the first plurality of images captured by the first sensor are images captured from a forward perspective of the autonomous vehicle.

5. The apparatus of claim 2, wherein the second plurality of images captured by the second sensor are images captured from a panoramic perspective of the autonomous vehicle.

6. The apparatus of claim 1, wherein:

the at least one object label comprises a plurality of parameters that define the object label; and

the plurality of parameters depend on an image sensor type used to capture the first image from the first plurality of images captured by the autonomous vehicle.

7. The apparatus of claim 1, wherein the processor is further operative to determine whether the received object label corresponds to the object label applied by the autonomous vehicle by determining whether the object label applied by the autonomous vehicle overlaps any portion of the received object label.

8. The apparatus of claim 1, wherein the processor is further operative to determine whether the received object label corresponds to the object label applied by the autonomous vehicle by determining an object identification ratio derived from the received object label and the object label applied by the autonomous vehicle.

9. The apparatus of claim 1, wherein the processor is operative to determine whether the received object label corresponds to the object label applied by the autonomous vehicle based on:

a first area represented by the intersection of an area of the received object label with an area of the object label applied by the autonomous vehicle; and

a second area represented by the union of the area of the received object label with the area of the object label applied by the autonomous vehicle.

10. The apparatus of claim 1, wherein the object label applied by the autonomous vehicle is based on the plurality of object detection parameters.

11. A method for optimizing object detection performed by an autonomous vehicle, the method comprising:

storing, in a memory, a first plurality of images captured by an autonomous vehicle, wherein a first image in the first plurality of images includes at least one applied object label applied by the autonomous vehicle; and

displaying, with a processor in communication with a memory, a first image from the first plurality of images, wherein the first image comprises at least one object;

receiving an object label for the at least one object displayed in the first image from the first plurality of images to obtain a received object label in a first image for a second plurality of images;

comparing the at least one received object label with at least one object label applied by the autonomous vehicle to the object in the first image in the first plurality of images;

determining whether the received object label corresponds to the object label applied by the autonomous vehicle;

compare a number of object labels applied by the autonomous vehicle in the first image in the first plurality of images with a number of object labels received in the first image in the second plurality of images;

determining whether the autonomous vehicle missed a predetermined number of object labels in the first image in the first plurality of images based on the comparison of the number of object labels applied by the autonomous vehicle in the first image from the first plurality of images and the number of object labels received in the first image from the second plurality of images; and

optimizing a plurality of object detection parameters of at least one sensor of a plurality of sensors when an indication of a correspondence between the received object label and the object label applied by the autonomous vehicle is below a predetermined correspondence threshold and the autonomous vehicle missed the predetermined number of object labels in the first image of the first plurality of images,

wherein the object label applied by the autonomous vehicle is defined by at least one object label parameter that corresponds to a type of sensor that captured the first image.

12. The method of claim 11, wherein the first plurality of images comprise a first plurality of images captured by a first sensor of a first sensor type and a second plurality of images captured by a second sensor of a second sensor type.

13. The method of claim 12, wherein the first sensor comprises a camera and the second sensor comprises a laser.

14. The method of claim 12, wherein the first plurality of images captured by the first sensor are images captured from a forward perspective of the autonomous vehicle.

15. The method of claim 12, wherein the second plurality of images captured by the second sensor are images captured from a panoramic perspective of the autonomous vehicle.

16. The method of claim 11, wherein:

the at least one object label comprises a plurality of parameters that define the object label; and

the plurality of parameters depend on an image sensor type used to capture the first image from the first plurality of images captured by the autonomous vehicle.

17. The method of claim 11, wherein determining whether the received object label corresponds to the object label applied by the autonomous vehicle comprises determining whether the object label applied by the autonomous vehicle overlaps any portion of the received object label.

18. The method of claim 11, wherein determining whether the received object label corresponds to the object label applied by the autonomous vehicle comprises determining an object identification ratio derived from the received object label and the object label applied by the autonomous vehicle.

19. The method of claim 11, wherein determining whether the received object label corresponds to the object label applied by the autonomous vehicle is based on:

a first area represented by the intersection of an area of the received object label with an area of the object label applied by the autonomous vehicle; and

a second area represented by the union of the area of the received object label with the area of the object label applied by the autonomous vehicle.

20. The method of claim 11, wherein the object label applied by the autonomous vehicle is based on the plurality of object detection parameters.

21. The method of claim 11, further comprising:

associating the object label applied by the autonomous vehicle with an image frame number that identifies the first image in the first plurality of images.