Predictive sensor array configuration system for an autonomous vehicle

• US 10,684,361 B2
• Filed: 11/30/2018
• Issued: 06/16/2020
• Est. Priority Date: 12/16/2015
• Status: Active Grant

• Current Assignee: Aurora Operations Incorporated (Aurora Innovation, Inc.)
• Original Assignee: Uatc, LLC (Uber Technologies, Inc.)
• Inventors: Valois, Jean-Sebastien
• Primary Examiner(s): Brushaber, Frederick M
• Application Number:
  US16/206,660
• Publication Number:
  US 20190113607A1
• Time in Patent Office: 564 Days
• CPC Class Codes: B60W 40/02 related to ambient conditions
• 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 13/931 of land vehicles
• 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: G01S 7/40 Means for monitoring or calibrating
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• CPC Class Codes: G05D 1/021 specially adapted to land vehicles
• CPC Class Codes: G05D 1/0231 using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• 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: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/38 for collecting sensor information
• CPC Class Codes: H04W 4/48 for in-vehicle communication
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks
• CPC Class Codes: Y04S 40/18 Network protocols supporting networked applications, e.g. including control of end-device applications over a network

Abstract

A sensor configuration system can determine imminent lighting conditions for one or more light sensing elements of an autonomous vehicle (AV), and can execute a set of configurations for the one or more cameras to preemptively compensate for the imminent lighting conditions.

Claims

1. A sensor configuration system for an autonomous vehicle (AV), comprising:

one or more light sensing elements;

one or more processors; and

one or more memory resources storing instructions that, when executed by the one or more processors, cause the sensor configuration system to;

determine an imminent lighting condition for the one or more light sensing elements; and

execute a set of configurations for the one or more light sensing elements to preemptively compensate for the imminent lighting condition.

2. The sensor configuration system of claim 1, wherein the AV stores a set of sub-maps comprising recorded surface data of a given region upon which the AV operates, and wherein the executed instructions cause the sensor configuration system to determine the imminent lighting condition by analyzing a current sub-map from the stored set of sub-maps.

3. The sensor configuration system of claim 2, wherein the stored set of sub-maps identify road features that affect the imminent lighting condition, the road features comprising at least one of a tunnel, an overpass, or proximate buildings.

4. The sensor configuration system of claim 2, wherein each sub-map in the set of sub-maps comprises at least one of recorded LIDAR data or recorded image data.

5. The sensor configuration system of claim 1, wherein the executed instructions further cause the sensor configuration system to:

dynamically determine a brightness differential between a current lighting condition and the imminent lighting condition;

wherein executed instructions cause the sensor configuration system to determine the set of configurations based on the brightness differential.

6. The sensor configuration system of claim 1, wherein execution of the set of configurations for the one or more light sensing elements comprises preemptively adjusting aperture settings of the one or more light sensing elements based on the imminent lighting condition.

7. The sensor configuration system of claim 1, wherein the executed instructions cause the sensor configuration system to determine the imminent lighting condition by analyzing sensor data from a set of sensors of the AV.

8. The sensor configuration system of claim 1, wherein changes in the imminent lighting condition correspond to at least one of shadows, lights, the Sun, or solar reflections.

9. The sensor configuration system of claim 1, wherein the executed instructions cause the sensor configuration system to determine the imminent light condition by receiving data indicating the imminent lighting condition from forward operating AVs in relation to the AV.

10. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

determine an imminent lighting condition for one or more light sensing elements of an autonomous vehicle (AV); and

execute a set of configurations for the one or more light sensing elements to preemptively compensate for the imminent lighting condition.

11. The non-transitory computer-readable medium of claim 10, wherein the AV stores a set of sub-maps comprising recorded surface data of a given region upon which the AV operates, and wherein the executed instructions cause the one or more processors to determine the imminent lighting condition by analyzing a current sub-map from the stored set of sub-maps.

12. The non-transitory computer-readable medium of claim 11, wherein the stored set of sub-maps identify road features that affect the imminent lighting condition, the road features comprising at least one of a tunnel, an overpass, or proximate buildings.

13. The non-transitory computer-readable medium of claim 11, wherein each sub-map in the set of sub-maps comprises at least one of recorded LIDAR data or recorded image data.

14. The non-transitory computer-readable medium of claim 10, wherein the executed instructions further cause the one or more processors to:

dynamically determine a brightness differential between a current lighting condition and the imminent lighting condition;

wherein executed instructions cause the one or more processors to determine the set of configurations based on the brightness differential.

15. The non-transitory computer-readable medium of claim 10, wherein execution of the set of configurations for the one or more light sensing elements comprises preemptively adjusting aperture settings of the one or more light sensing elements based on the imminent lighting condition.

16. The non-transitory computer-readable medium of claim 10, wherein the executed instructions cause the one or more processors to determine the imminent lighting condition by analyzing sensor data from a set of sensors of the AV.

17. The non-transitory computer-readable medium of claim 10, wherein the executed instructions cause the one or more processors to determine the imminent light condition by receiving data indicating the imminent lighting condition from forward operating AVs in relation to the AV.

18. A computer-implemented method of preemptively configuring sensors for an autonomous vehicle (AV), the method being performed by one or more processors and comprising:

determining an imminent lighting condition for one or more light sensing elements of the AV; and

executing a set of configurations for the one or more light sensing elements to preemptively compensate for the imminent lighting condition.

19. The method of claim 18, wherein the AV stores a set of sub-maps comprising recorded surface data of a given region upon which the AV operates, and wherein the one or more processors determine the imminent lighting condition by analyzing a current sub-map from the stored set of sub-maps.

20. The method of claim 19, wherein the stored set of sub-maps identify road features that affect the imminent lighting condition, the road features comprising at least one of a tunnel, an overpass, or proximate buildings.