Puddle occupancy grid for autonomous vehicles

Patent metadata

Field	Value
Publication number	US-12060080-B2
Application number	US-202318310620-A
Country	US
Kind code	B2
Filing date	May 2, 2023
Priority date	Dec 11, 2020
Publication date	Aug 13, 2024
Grant date	Aug 13, 2024

Abstract

Aspects of the disclosure relate to generating a puddle occupancy grid including a plurality of cells. For instance, a first probability value for a puddle being located at a first location generated using sensor data from a first sensor may be received. A second probability value for a puddle being located at a second location generating using sensor data from a second sensor different from the first sensor may be received. A first cell may be identified from the plurality of cells using the first location. The first cell may also be identified using the second location. A value for the cell may be generated using the first probability value and the second probability value.

First claim

The invention claimed is:

1. A method comprising:
receiving, by one or more processors, a first probability value for puddling at a first location generated using sensor data from a first sensor;
generating, by the one or more processors, a first value for a first cell of a plurality of cells of a puddle occupancy grid based on the first location and the first probability value;
sending, by the one or more processors, the first value to a remote computing system;
receiving, by the one or more processors, a second value representing a second probability for puddling at a second location from the remote computing system;
incorporating, by the one or more processors, the second value into the puddle occupancy grid; and
using, by the one or more processors, the puddle occupancy grid to control a vehicle in an autonomous driving mode.

2. The method of claim 1 , wherein the remote computing system is part of another vehicle.

3. The method of claim 1 , wherein the remote computing system is a remote server computing system.

4. The method of claim 1 , wherein the second value is received as part of a second puddle occupancy grid.

5. The method of claim 1 , wherein generating the first value for the first cell includes first incorporating the first probability value and subsequently incorporating an additional probability value generated using second sensor data.

6. The method of claim 5 , wherein incorporating the first probability value includes using an inverse logistic regression function.

7. The method of claim 6 , wherein incorporating the additional probability value includes using the inverse logistic regression function.

8. The method of claim 1 , wherein generating the first value for the first cell includes using a Sigmoid function to convert the first probability value to a probability of puddling at the first cell.

9. The method of claim 1 , wherein using the puddle occupancy grid includes clustering cells of the occupancy grid, and wherein controlling the vehicle is further based on the clustered cells.

10. The method of claim 1 , further comprising sending the puddle occupancy grid to the remote computing system.

11. The method of claim 1 , wherein using the puddle occupancy grid to control the vehicle includes disregarding portions of sensor data.

12. The method of claim 11 , wherein disregarding portions of sensor data includes filtering out splashes from sensor data.

13. The method of claim 11 , wherein disregarding portions of sensor data includes adjusting a threshold value for classification of splashes.

14. The method of claim 1 , wherein using the puddle occupancy grid to control the vehicle includes taking a detour to avoid a blockage related to puddling and traffic caused by the blockage.

15. The method of claim 1 , wherein using the puddle occupancy grid to control the vehicle includes estimating changes in friction.

16. The method of claim 15 , further comprising, using the estimated changes in friction to control one of a path or speed of the vehicle.

17. A system comprising one or more processors configured to:
receive a first probability value for puddling at a first location generated using sensor data from a first sensor;
generate a first value for a first cell of a plurality of cells of a puddle occupancy grid based on the first location and the first probability value;
send the first value to a remote computing system;
receive a second value representing a second probability for puddling at a second location from the remote computing system;
incorporate the second value into the puddle occupancy grid; and
use the puddle occupancy grid to control a vehicle in an autonomous driving mode.

18. The system of claim 17 , wherein the one or more processors are further configured to send the puddle occupancy grid to the remote computing system.

19. The system of claim 17 , wherein the one or more processors are further configured to use the puddle occupancy grid to control the vehicle by disregarding portions of sensor data.

20. The system of claim 17 , wherein the one or more processors are further configured to use the puddle occupancy grid to control the vehicle by taking a detour to avoid a blockage related to puddling and traffic caused by the blockage.

Assignees

• Waymo Llc

Inventors

• Wang Yuchi
• James Michael
• Chen Robert
• Dominic Derrick

Classifications

• B60W2420/408

  Radar; Laser, e.g. lidar · CPC title

• G06F18/2415

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• G06F18/23

  Clustering techniques · CPC title

• G06V20/588

  Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road · CPC title

• B60W40/06

  Road conditions · CPC title