Detecting sensor degradation by actively controlling an autonomous vehicle

• US 10,591,924 B1
• Filed: 04/22/2019
• Issued: 03/17/2020
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Ferguson, David I, Zhu, Jiajun
• Primary Examiner(s): Ingram, Thomas
• Application Number:
  US16/390,106
• Time in Patent Office: 330 Days
• 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. A system for determining operating status of a sensor, the system comprising

one or more processors, in communication with a sensor of a first vehicle, the one or more processors being configured to:

identify at least one movement characteristic of the first vehicle;

receive sensor data from the sensor, wherein the sensor data includes data for a second vehicle in a driving environment of the first vehicle;

determine a first state information for the second vehicle based on the sensor data;

determine a second state information for the second vehicle based on the at least one movement characteristic;

compare the first state information with the second state information; and

determine the operating status of the sensor based on the comparison.

2. The system of claim 1, wherein determining the operating status is further based on at least one static object included in the sensor data.

3. The system of claim 1, wherein the one or more processors are further configured to determine a deviation value based on the comparison, and wherein the deviation value is further used to determine the operating status of the sensor.

4. The system of claim 3, wherein the one or more processors are further configured to determine if the deviation value meets a threshold value, and wherein the determination is further used to determine the operating status.

5. The system of claim 4, wherein when the determination includes that the deviation value not meets the threshold value, and the operating status corresponds to the sensor being degraded.

6. The system of claim 3, wherein the one or more processors are further configured to determine a second deviation value, and wherein the second deviation value is further used to determine the operating status of the sensor.

7. The system of claim 1, wherein the one or more processors are further configured to, in response to determining the operating status of the sensor, requesting that a driver take control the vehicle.

8. The system of claim 1, further including a display, and wherein the one or more processors are further configured to, in response to determining the operating status of the sensor, displaying a notification on the display indicating degradation of the sensor.

9. The system of claim 1, wherein:

the second state information comprises an expected parameter; and

determining the second state information comprises;

determining a change to the at least one movement characteristic of the first vehicle; and

determining the expected parameter based on the change to the at least one movement characteristic of the first vehicle.

10. The system of claim 9, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in distance between the first vehicle and the second vehicle.

11. The system of claim 9, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in the speed of the first vehicle.

12. The system of claim 1, further comprising the first vehicle and the sensor, wherein the one or more processors are located in the first vehicle.

13. A non-transitory computer-readable medium on which instructions are stored, the instructions, when executed by one or more processors, cause the one or more processors to perform a method for determining operating status of a sensor, the method comprising:

identifying at least one movement characteristic of a first vehicle;

receiving sensor data from the sensor, wherein the sensor data includes data for a second vehicle in a driving environment of the first vehicle;

determining a first state information for the second vehicle based on the sensor data;

determining a second state information for the second vehicle based on the at least one movement characteristic;

comparing the first state information with the second state information; and

determining the operating status of the sensor based on the comparison.

14. The medium of claim 13, wherein the method further includes determining the operating status is further based on at least one static object included in the sensor data.

15. The medium of claim 13, wherein the method further includes determining a deviation value based on the comparison, and wherein the deviation value is further used to determine the operating status of the sensor.

16. The medium of claim 15, wherein the method further includes determining if the deviation value meets a threshold value, and wherein the determination is further used to determine the operating status.

17. The medium of claim 16, wherein when the determination includes that the deviation value not meets the threshold value, and the operating status corresponds to the sensor being degraded.

18. The medium of claim 15, wherein the method further comprises determining a second deviation value, and wherein the second deviation value is further used to determine the operating status of the sensor.

19. The medium of claim 13, wherein the second state information comprises an expected parameter, and determining the second state information comprises:

determining a change to the at least one movement characteristic of the first vehicle; and

determining the expected parameter based on the change to the at least one movement characteristic of the first vehicle.

20. The medium of claim 19, wherein the change to the at least one movement characteristic of the first vehicle corresponds to a change in the speed of the first vehicle.