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

• US 10,572,717 B1
• Filed: 02/27/2019
• Issued: 02/25/2020
• Est. Priority Date: 10/05/2010
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Zhu, Jiajun, Urmson, Christopher Paul, Haehnel, Dirk, Fairfield, Nathaniel, Smith, Russell Leigh
• Primary Examiner(s): Yang, Wei Wen
• Application Number:
  US16/209,429
• Time in Patent Office: 363 Days
• Field of Search: None
• 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. A method for optimizing object detection performed by autonomous vehicles, the method comprising:

receiving, by one or more computing devices having one or more processors, sensor data captured by a sensor of an autonomous vehicle, wherein the sensor data includes one or more first object labels applied by the autonomous vehicle, each of the one or more first object labels corresponding to an object of the sensor data;

receiving, by the one or more computing devices, one or more second object labels not applied by the autonomous vehicle for the sensor data;

determining, by the one or more computing devices, a difference between a first value for the one or more first object labels applied by the autonomous vehicle and a second value for the one or more second object labels;

comparing, by the one or more computing devices, the determined differences to a threshold value, wherein the threshold value is selected based on a type of sensor that generated the sensor data; and

generating, by the one or more computing devices, a recommendation for an adjustment of at least one object detection parameter of at least one of a plurality of sensors based on the comparison in order to optimize object detection.

2. The method of claim 1, wherein the type of sensor is one of a camera, a LIDAR sensor, or a radar.

3. The method of claim 1, wherein the first value is a quantity of the one or more first object labels.

4. The method of claim 1, wherein the second value is a quantity of the one or more second object labels.

5. The method of claim 1, wherein the threshold is a predetermined missed object threshold corresponding to an absolute number of objects that are undetected by the autonomous vehicle.

6. The method of claim 1, wherein the threshold is a predetermined missed object threshold corresponding to a percentage of objects that are undetected by the autonomous vehicle.

7. The method of claim 1, further comprising determining an accuracy of each given object label of the first set of object labels by determining an object label ratio based on an area of intersection between the given object label and one of the second set of object labels, and wherein the recommendation is further based on the accuracy.

8. The method of claim 7, wherein generating the recommendation is further based on whether each object label ratio identifies a direct correlation between the first set of object labels and the second set of object labels.

9. The method of claim 7, wherein generating the recommendation is further based on whether each object label ratio identifies an imperfect correlation between the first set of object labels and the second set of object labels.

10. The method of claim 7, wherein determining the object label ratio for each given object label is further based on an area of union between the given object label and the one of the second set of object labels.

11. The method of claim 10, wherein the object label ratio for each given object label divides the area of intersection for the given object label by the area of union for the given object label and the one of the second set of object labels.

12. The method of claim 7, further comprising determining a mean object label ratio value from any determined object ratio labels, wherein the recommendation is further based on the mean object label ratio.

13. The method of claim 12, further comprising comparing the mean object label ration to a second threshold value, wherein the second threshold value is selected based on the type of sensor.

14. A method for optimizing object detection performed by autonomous vehicles, the method comprising:

receiving, by one or more computing devices having one or more processors, sensor data captured by a sensor of an autonomous vehicle, wherein the sensor data includes one or more first object labels applied by the autonomous vehicle, each of the one or more first object labels corresponding to an object of the sensor data;

receiving, by the one or more computing devices, one or more second object labels not applied by the autonomous vehicle for the sensor data;

determining, by the one or more computing devices, a difference between a first value indicating a quantity of the one or more first object labels applied by the autonomous vehicle and a second value indicating a quantity of the one or more second object labels;

comparing, by the one or more computing devices, the determined differences to a threshold value, wherein the threshold value is selected based on a type of sensor that generated the sensor data; and

generating, by the one or more computing devices, a recommendation for an adjustment of at least one object detection parameter of at least one of a plurality of sensors based on the comparison in order to optimize object detection.

15. The method of claim 14, wherein the type of sensor is a camera.

16. The method of claim 14, wherein the type of sensor is a LIDAR sensor.

17. The method of claim 14, wherein the threshold is a predetermined missed object threshold corresponding to an absolute number of objects that are undetected by the autonomous vehicle.

18. The method of claim 14, wherein the threshold is a predetermined missed object threshold corresponding to a percentage of objects that are undetected by the autonomous vehicle.

19. The method of claim 14, further comprising determining an accuracy of each given object label of the first set of object labels by determining an object label ratio based on an area of intersection between the given object label and one of the second set of object labels, and wherein the recommendation is further based on the accuracy.

20. The method of claim 19, wherein determining the object label ratio for each given object label is further based on an area of union between the given object label and the one of the second set of object labels.