IP Library Granted Patent US 10,712,742
Granted Patent B2
US 10,712,742 · App. 15/716,144 · Granted Jul 14, 2020

Predictive sensor array configuration system for an autonomous vehicle

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Quick Facts
Patent No.
US 10,712,742
App. No.
15/716,144
Granted
Jul 14, 2020
Kind
B2
Abstract

An autonomous vehicle (AV) can include a predictive sensor configuration system that can dynamically detect reflectance anomalies that affect detectability by sensor array of the AV as the AV travels a current route. The predictive sensor configuration system can dynamically determine one or more configurations for the sensor array to attempt to positively identify the reflectance anomalies, and preemptively execute the one or more configurations for the sensor array as the AV travels the current route.

Claims (47)

1. An autonomous vehicle (AV) comprising:

a LIDAR sensor generating sensor data corresponding to a surrounding environment of the AV; and

a control system comprising one or more processors executing an instruction set, causing the control system to:

analyze a current sub-map comprising previously recorded surface data of a current route of the AV to predict one or more imminent reflectance anomalies for a future location of the AV on the current route of the AV;

store, in a database, a lookup table (LUT) comprising a plurality of sensor configurations for the LIDAR sensor;

based on the one or more predicted imminent reflectance anomalies, perform a lookup in the LUT to determine a set of configurations for the LIDAR sensor; and

preemptively execute the set of configurations on one or more adjustable parameters of the LIDAR sensor as the AV approaches the future location of the AV on the current route of the AV.

2. The AV of claim 1 , wherein the executed instruction set further causes the control system to:

store a set of sub-maps comprising recorded surface data of a given region upon which the AV operates;

wherein the current sub-map is included in the set of sub-maps.

3. The AV 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.

4. The AV of claim 1 , further comprising:

acceleration, braking, and steering systems;

wherein the executed instruction set further causes the control system to:

based on comparing the sensor data to the current sub-map, autonomously operate the acceleration, braking, and steering systems along the current route;

wherein the executed instruction set causes the control system

to predict the one or more imminent reflectance anomalies as the AV travels along the current route.

5. The AV of claim 1 , wherein preemptively executing the set of configurations for the LIDAR sensor as the AV approaches the future location on the current route of the AV comprises adjusting at least one of a beam frequency, a laser power level, or a scan rate of the LIDAR sensor.

6. The AV of claim 1 , wherein preemptively executing the set of configurations on the LIDAR sensor enables the control system to resolve the one or more imminent reflectance anomalies, and wherein the executed instruction set further causes the control system to:

based on resolving the one or more imminent reflectance anomalies, establish a mesh network with one or more rearward AVs; and

transmit data identifying the one or more imminent reflectance anomalies to the one or more rearward AVs.

7. The AV of claim 1 , wherein the one or more imminent reflectance anomalies correspond to at least one of a dark object, a shiny object, an organic material, dust, smoke, fog, or precipitation.

8. The AV of claim 6 , wherein the executed instruction set causes the control system to resolve the one or more imminent reflectance anomalies by positively identifying each of the one or more imminent reflectance anomalies.

9. The AV of claim 6 , wherein the executed instruction set causes the control system to resolve each imminent reflectance anomaly of the one or more imminent reflectance anomalies by determining whether each imminent reflectance anomaly comprises a critical object or a neglectable object.

10. A non-transitory computer readable medium storing an instruction set that, when executed by one or more processors of a control system of an autonomous vehicle (AV), causes the control system to:

analyze a current sub-map comprising previously recorded surface data of a current route of the AV to predict one or more imminent reflectance anomalies for a future location of the AV on the current route of the AV;

store, in a database, a lookup table (LUT) comprising a plurality of sensor configurations for a LIDAR sensor;

based on the one or more predicted imminent reflectance anomalies, perform a lookup in the LUT to determine a set of configurations for the LIDAR sensor; and

preemptively execute the set of configurations on one or more adjustable parameters of the LIDAR sensor as the AV approaches the future location of the AV on the current route of the AV.

11. The non-transitory computer readable medium of claim 10 , wherein the executed instruction set further causes the control system to:

store a set of sub-maps comprising recorded surface data of a given region upon which the AV operates;

wherein the current sub-map is included in the set of sub-maps.

12. 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.

13. The non-transitory computer readable medium of claim 10 , wherein the AV comprises acceleration, braking, and steering systems, and wherein the executed instruction set further causes the control system to:

based on comparing sensor data from the LIDAR sensor to the current sub-map, autonomously operate the acceleration, braking, and steering systems along the current route;

wherein the executed instruction set causes the control system to determine the one or more imminent reflectance anomalies as the AV travels along the current route.

14. The non-transitory computer readable medium of claim 10 , wherein executing the set of configurations for the LIDAR sensor comprises adjusting at least one of a beam frequency, a laser power level, or a scan rate of the LIDAR sensor.

15. The non-transitory computer readable medium of claim 10 , wherein executing the set of configurations on the LIDAR sensor enables the control system to resolve the one or more imminent reflectance anomalies, and wherein the executed instruction set further causes the control system to:

based on resolving the one or more imminent reflectance anomalies, establish a mesh network with one or more rearward AVs; and

transmit data identifying the one or more imminent reflectance anomalies to the one or more rearward AVs.

16. The non-transitory computer readable medium of claim 10 , wherein the one or more imminent reflectance anomalies correspond to at least one of a dark object, a shiny object, an organic material, dust, smoke, fog, or precipitation.

17. The non-transitory computer readable medium of claim 15 , wherein the executed instruction set causes the control system to resolve the one or more imminent reflectance anomalies by positively identifying each of the one or more imminent reflectance anomalies.

18. A computer-implemented method of resolving reflectance anomalies for a LIDAR sensor of an autonomous vehicle (AV), the method being performed by one or more processors of the AV and comprising:

analyzing, by a computing system comprising one or more computing devices, a current sub-map comprising previously recorded surface data of a current route of the AV to predict one or more imminent reflectance anomalies for a future location of the AV on the current route of the AV;

storing, by the computing system, in a database, a lookup table (LUT) comprising a plurality of sensor configurations for the LIDAR sensor;

based on the one or more predicted imminent reflectance anomalies, performing, by the computing system, a lookup in the LUT to determine a set of configurations for the LIDAR sensor; and

preemptively executing, by the computing system, the set of configurations on one or more adjustable parameters of the LIDAR sensor as the AV approaches the future location of the AV on the current route of the AV.

Assignments (4)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 14, 2024
From: UATC, LLC
To: AURORA OPERATIONS, INC.
Reel/Frame 067733/0001 →
CORRECTIVE ASSIGNMENT TO CORRECT THE NATURE OF CONVEYANCE FROM CHANGE OF NAME TO ASSIGNMENT PREVIOUSLY RECORDED ON REEL 050353 FRAME 0884. ASSIGNOR(S) HEREBY CONFIRMS THE CORRECT CONVEYANCE SHOULD BE ASSIGNMENT. Recorded Nov 27, 2019
From: UBER TECHNOLOGIES, INC.
To: UATC, LLC
Reel/Frame 051145/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 26, 2019
From: VALOIS, JEAN-SEBASTIAN
To: UBER TECHNOLOGIES, INC.
Reel/Frame 050500/0019 →
CHANGE OF NAME Recorded Sep 12, 2019
From: UBER TECHNOLOGIES, INC.
To: UATC, LLC
Reel/Frame 050353/0884 →