Detecting sensor degradation by actively controlling an autonomous vehicle

• US 9,594,379 B1
• Filed: 02/08/2016
• Issued: 03/14/2017
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Ferguson, David I., Zhu, Jiajun
• Primary Examiner(s): Troost, Aaron L
• Assistant Examiner(s): Ingram, Thomas
• Application Number:
  US15/018,205
• Time in Patent Office: 400 Days
• US Class Current: 1/1
• CPC Class Codes: B60K 31/0008 including means for detecting potential obstacles in vehicle path
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/18 including control of braking systems
• CPC Class Codes: B60W 10/20 including control of steering systems
• CPC Class Codes: B60W 2050/0215 Sensor drifts or sensor failures
• CPC Class Codes: B60W 2050/146 Display means
• 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 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2554/801 Lateral distance
• CPC Class Codes: B60W 2556/20 Data confidence level
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 2720/24 Direction of travel
• CPC Class Codes: B60W 30/12 Lane keeping
• CPC Class Codes: B60W 30/14 Adaptive cruise control
• CPC Class Codes: B60W 50/0205 : Diagnosing or detecting failures; Failure detection models
• CPC Class Codes: B60W 50/029 : Adapting to failures or work around with other constraints, e.g. circumvention by avoiding use of failed parts
• CPC Class Codes: B60W 60/00186 : related to the vehicle
• CPC Class Codes: B60W 60/0053 : from vehicle to occupant
• CPC Class Codes: B60W 60/0059 : Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
• CPC Class Codes: G01C 21/28 : with correlation of data from several navigational instruments
• CPC Class Codes: G01S 13/58 : Velocity or trajectory determination systems; Sense-of-movement determination systems
• CPC Class Codes: G01S 13/931 : of land vehicles
• CPC Class Codes: G01S 17/66 : Tracking systems using electromagnetic waves other than radio waves
• 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 17/87 : Combinations of systems using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 2007/4975 : of sensor obstruction by, e.g. dirt- or ice-coating, e.g. by reflection measurement on front-screen
• CPC Class Codes: G01S 7/4004 : of parts of a radar system
• CPC Class Codes: G01S 7/4039 : of sensor or antenna obstruction, e.g. dirt- or ice-coating
• CPC Class Codes: G01S 7/497 : Means for monitoring or calibrating
• CPC Class Codes: G01S 7/4972 : Alignment of sensor
• CPC Class Codes: G01S 7/52004 : Means for monitoring or calibrating short-range imaging G01S7/5205
• CPC Class Codes: G05D 1/00 : Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots drive control systems specially adapted for autonomous road vehicles B60W60/00
• 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 1/0251 : 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: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• 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

Abstract

Methods and systems are disclosed for determining sensor degradation by actively controlling an autonomous vehicle. Determining sensor degradation may include obtaining sensor readings from a sensor of an autonomous vehicle, and determining baseline state information from the obtained sensor readings. A movement characteristic of the autonomous vehicle, such as speed or position, may then be changed. The sensor may then obtain additional sensor readings, and second state information may be determined from these additional sensor readings. Expected state information may be determined from the baseline state information and the change in the movement characteristic of the autonomous vehicle. A comparison of the expected state information and the second state information may then be performed. Based on this comparison, a determination may be made as to whether the sensor has degraded.

Claims

1. An apparatus, comprising:

a computer-readable memory;

a radar detection unit configured to detect an observed vehicle in a driving environment of the autonomous vehicle; and

one or more processors, in communication with the sensor and the computer-readable memory, the one or more processors configured to;

introduce a perturbation to a yaw value of the autonomous vehicle, wherein the perturbation causes a movement of the autonomous vehicle;

detect a lateral speed of the observed vehicle;

determine whether the detected lateral speed of the observed vehicle is within a range of expected lateral speeds of the observed vehicle;

determine that the radar detection unit is operating outside of normal operating parameters if the observed lateral speeds are not within the range of expected lateral speeds; and

perform an action in response to a determination that the radar detection unit is operating outside of normal operating parameters.

2. The apparatus of claim 1, wherein the perturbation includes a turn of a steering wheel of the autonomous vehicle.

3. The apparatus of claim 1, wherein the expected range of lateral speeds includes a direction and a magnitude.

4. The apparatus of claim 3, wherein the magnitude is relative to a distance between the observed vehicle and the autonomous vehicle.

5. The apparatus of claim 3, wherein the direction of the expected range of lateral speeds is opposite a direction of the perturbation.

6. The apparatus of claim 1, wherein the perturbation is slight.

7. The apparatus of claim 1, wherein the observed vehicle is traveling in front of the autonomous vehicle.

14. The method of claim 1, wherein the observed vehicle is traveling in front of the autonomous vehicle.

8. A method, comprising:

introducing, with one or more processors, a perturbation to a yaw value of an autonomous vehicle, wherein the perturbation causes a movement of the autonomous vehicle;

detecting, with a radar detection unit in communication with the one or more processors, a lateral speed of an observed vehicle in a driving environment of the autonomous vehicle;

determining, with the one or more processors, whether the detected lateral speed of the observed vehicle is within a range of expected lateral speeds of the observed vehicle;

determining, with the one or more processors, that the radar detection unit is operating outside of normal operating parameters if the observed lateral speeds are not within the range of expected lateral speeds; and

performing an action in response to determining that the radar detection unit is operating outside of normal operating parameters.

9. The method of claim 8, wherein the perturbation includes a turn of a steering wheel of the autonomous vehicle.

10. The method of claim 8, wherein the expected range of lateral speeds includes a direction and a magnitude.

11. The method of claim 10, wherein the magnitude is relative to a distance between the observed vehicle and the autonomous vehicle.

12. The method of claim 10, wherein the direction of the expected range of lateral speeds is opposite a direction of the perturbation.

13. The method of claim 8, wherein the perturbation is slight.

15. A non-transitory computer-readable medium storing instructions executable by one or more processors for performing a method, comprising:

introducing a perturbation to a yaw value of an autonomous vehicle, wherein the perturbation causes a movement of the autonomous vehicle;

detecting, with a radar detection unit, a lateral speed of an observed vehicle in a driving environment of the autonomous vehicle;

determining whether the detected lateral speed of the observed vehicle is within a range of expected lateral speeds of the observed vehicle;

determining that the radar detection unit is operating outside of normal operating parameters if the observed lateral speeds are not within the range of expected lateral speeds; and

performing an action in response to determining that the radar detection unit is operating outside of normal operating parameters.

16. The computer-readable medium of claim 15, wherein the perturbation includes a turn of a steering wheel of the autonomous vehicle, the turn being slight.

17. The computer-readable medium of claim 15, wherein the expected range of lateral speeds includes a direction and a magnitude.

18. The computer-readable medium of claim 17, wherein the magnitude is relative to a distance between the observed vehicle and the autonomous vehicle.

19. The computer-readable medium of claim 17, wherein the direction of the expected range of lateral speeds is opposite a direction of the perturbation.

20. The computer-readable medium of claim 15, wherein the observed vehicle is traveling in front of the autonomous vehicle.