Systems and methods for autonomous vehicle controls

Patent metadata

Field	Value
Publication number	US-11964673-B2
Application number	US-202217705789-A
Country	US
Kind code	B2
Filing date	Mar 28, 2022
Priority date	Oct 9, 2019
Publication date	Apr 23, 2024
Grant date	Apr 23, 2024

Abstract

Systems and methods for controlling autonomous vehicle are provided. A method can include obtaining, by a computing system, data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle. The method can further include determining, by the computing system, one or more clusters of the objects based at least in part on the data indicative of the plurality of objects. The method can further include determining, by the computing system, whether to enter an operation mode having one or more limited operational capabilities based at least in part on one or more properties of the one or more clusters. In response to determining that the operation mode is to be entered by the autonomous vehicle, the method can include controlling, by the computing system, the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities.

First claim

What is claimed is:

1. A method to control operation of an autonomous vehicle, comprising:
obtaining data indicative of a plurality of objects in a surrounding environment of the autonomous vehicle;
entering an operation mode having one or more limited operational capabilities based at least in part on a quantity of the plurality of objects;
controlling the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities of the operation mode; and
pruning one or more objects of the plurality of objects from a prediction or motion planning analysis based the plurality of objects being positioned at a distance greater than a threshold distance from the autonomous vehicle.

2. The method of claim 1 , wherein entering the operation mode having the one or more limited operational capabilities based at least in part on the quantity of the plurality of objects comprises:
determining a total quantity of the plurality of objects in the surrounding environment of the autonomous vehicle; and
entering the operation mode having the one or more limited operational capabilities based at least in part on determining that the total quantity of the plurality of objects exceeds a threshold quantity of objects.

3. The method of claim 2 , further comprising:
entering the operation mode having the one or more limited operational capabilities based at least in part on an ability associated with an autonomy system of the autonomous vehicle.

4. The method of claim 3 , wherein the threshold quantity of objects is based on the ability associated with the autonomy system of the autonomous vehicle.

5. The method of claim 1 , wherein entering the operation mode having the one or more limited operational capabilities based at least in part on the quantity of the plurality of objects comprises:
determining one or more clusters of the plurality of objects based at least in part on the data indicative of the plurality of objects;
determining a total quantity of objects in at least one of the one or more clusters; and
entering the operation mode having the one or more limited operational capabilities based at least in part on determining that the total quantity of objects exceeds a threshold quantity of objects.

6. The method of claim 5 , wherein determining the one or more clusters of the plurality of objects comprises:
grouping one or more objects of the plurality of objects that are less than a threshold distance from one another into a respective cluster of the one or more clusters.

7. The method of claim 5 , wherein entering the operation mode having the one or more limited operational capabilities based at least in part on the quantity of the plurality of objects comprises:
determining that an area of at least one of the one or more clusters is greater than a threshold area.

8. The method of claim 1 , wherein the plurality of objects comprises a plurality of pedestrians or bicyclists.

9. The method of claim 1 , wherein the one or more limited operational capabilities comprise a vehicle speed restriction.

10. The method of claim 9 , wherein the method further comprises:
determining a distance between one or more objects of the plurality of objects and the autonomous vehicle; and
determining the vehicle speed restriction based on the distance.

11. The method of claim 9 , wherein the threshold distance from the autonomous vehicle is based on the vehicle speed restriction.

12. A computing system comprising:
one or more processors; and
one or more tangible, non-transitory, computer readable media that store instructions for execution by the one or more processors to cause the computing system to perform operations, the operations comprising:
obtaining data indicative of a plurality of objects in a surrounding environment of an autonomous vehicle;
entering an operation mode having one or more limited operational capabilities based at least in part on a quantity of the plurality of objects;

controlling the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities of the operation mode; and
pruning one or more objects of the plurality of objects from a prediction or motion planning analysis based the plurality of objects being positioned at a distance greater than a threshold distance from the autonomous vehicle.

13. The computing system of claim 12 , wherein the surrounding environment comprises an intersection and the one or more limited operational capabilities comprise a right turn on red restriction.

14. The computing system of claim 12 , wherein the surrounding environment comprises an intersection and the one or more limited operational capabilities comprise an unprotected left turn restriction.

15. The computing system of claim 12 , wherein the one or more limited operational capabilities comprise a vehicle speed restriction, and wherein the threshold distance from the autonomous vehicle is based on the vehicle speed restriction.

16. The computing system of claim 12 , wherein the plurality of objects in the surrounding environment of the autonomous vehicle comprises a crowd of pedestrians.

17. One or more non-transitory, computer-readable media storing instructions that are executable by one or more processors to cause the one or more processors to perform operations, the operations comprising:
obtaining data indicative of a plurality of objects in a surrounding environment of an autonomous vehicle;
entering an operation mode having one or more limited operational capabilities based at least in part on a quantity of the plurality of objects;

controlling the operation of the autonomous vehicle based at least in part on the one or more limited operational capabilities of the operation mode; and
pruning one or more objects of the plurality of objects from a prediction or motion planning analysis based the plurality of objects being positioned at a distance greater than a threshold distance from the autonomous vehicle.

18. The one or more non-transitory, computer-readable media of claim 17 , wherein entering the operation mode having the one or more limited operational capabilities based at least in part on the quantity of the plurality of objects comprises:
determining one or more clusters of the plurality of objects based at least in part on the data indicative of the plurality of objects;
determining a total quantity of objects in at least one of the one or more clusters; and
entering the operation mode having the one or more limited operational capabilities based at least in part on determining that the total quantity of objects exceeds a threshold quantity of objects.

19. The one or more non-transitory, computer-readable media of claim 18 , wherein entering the operation mode having the one or more limited operational capabilities based at least in part on the quantity of the plurality of objects comprises:
determining that an area of at least one of the one or more clusters is greater than a threshold area.

Assignees

• Uatc Llc

Inventors

• Becker Brian C
• Phillips Michael Lee
• Xu Yang
• Perko Eric Michael
• Melik-Barkhudarov Narek

Classifications

• B60W60/0018 Primary

  by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions · CPC title

• B60W30/085

  Taking automatic action to adjust vehicle attitude in preparation for collision, e.g. braking for nose dropping · CPC title

• G05D1/0223

  involving speed control of the vehicle (vehicle fittings for automatically controlling, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator B60K31/00) · CPC title

• G06V10/762

  using clustering, e.g. of similar faces in social networks · CPC title

• G06V20/58

  Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads · CPC title