Detecting sensor degradation by actively controlling an autonomous vehicle

• US 10,310,509 B1
• Filed: 02/09/2018
• Issued: 06/04/2019
• 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:
  US15/892,684
• Time in Patent Office: 480 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:

the sensor configured to generate sensor data including data for a first vehicle in a driving environment of a second vehicle; and

one or more processors, in communication with the sensor, the one or more processors being configured to;

control the second vehicle in an autonomous driving mode according to at least one movement characteristic;

receive the sensor data from the sensor;

compare the sensor data with expected state information for the first vehicle based on the at least one movement characteristic; and

use the comparison to determine the operating status of the sensor.

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 use the comparison to determine a deviation value, 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 expected state information corresponds to state information for the first vehicle that would be expected based on a change to the at least one movement characteristic of the second vehicle.

10. The system of claim 9, wherein the expected state information corresponds to an expected change in distance between the first vehicle and the second vehicle.

11. The method of claim 10, wherein the movement characteristic relates to a speed of the second vehicle and the change corresponds to a change in the speed.

12. The system of claim 9, wherein the movement characteristic relates to a speed of the second vehicle and the change corresponds to a change in the speed.

13. The system of claim 1, further comprising the second vehicle.

14. A method for determining operating status of a sensor, the method comprising:

receiving, by one or more processors, sensor data from the sensor, the sensor data including data for a first vehicle in a driving environment of a second vehicle;

controlling, by the one or more processors, a second vehicle in an autonomous driving mode according to at least one movement characteristic;

comparing, by the one or more processors, the sensor data with expected state information for the first vehicle based on the at least one movement characteristic; and

using, by one or more processors, the comparison to determine an operating status of the sensor.

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

16. The method of claim 14, further comprising using the comparison to determine a deviation value, and wherein the deviation value is further used to determine the operating status of the sensor.

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

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

19. The method of claim 16, 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.

20. The method of claim 14, wherein the expected state information corresponds to state information for the first vehicle that would be expected based on a change to the at least one movement characteristic of the second vehicle.