Control of autonomous vehicle based on determined yaw parameter(s) of additional vehicle

Patent metadata

Field	Value
Publication number	US-11964663-B2
Application number	US-202318133509-A
Country	US
Kind code	B2
Filing date	Apr 11, 2023
Priority date	Apr 11, 2018
Publication date	Apr 23, 2024
Grant date	Apr 23, 2024

Abstract

Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

First claim

What is claimed is:

1. A method comprising:
receiving, from a phase coherent Light Detection and Ranging (LIDAR) component of a vehicle, a group of LIDAR data points of a sensing cycle of the phase coherent LIDAR component,
each of the LIDAR data points of the group indicating a corresponding instantaneous range and a corresponding instantaneous velocity for a corresponding point in an environment of the vehicle, and each of the LIDAR data points of the group being generated based on a corresponding sensing event of the phase coherent LIDAR component during the sensing cycle;
determining that a subgroup, of the LIDAR data points of the group, corresponds to a dynamic object in the environment;
based on determining that the subgroup corresponds to the dynamic object:
determining at least one instantaneous vehicle characteristic of the dynamic object based on a plurality of the corresponding instantaneous velocities of the LIDAR data points of the subgroup; and
adapting autonomous control of the vehicle based on the determined at least one instantaneous vehicle characteristic of the dynamic object.

2. The method of claim 1 ,
wherein determining the at least one instantaneous vehicle characteristic of the dynamic object based on the plurality of the corresponding instantaneous velocities of the LIDAR data points of the subgroup comprises:
determining the at least one instantaneous vehicle characteristic based on the plurality of the corresponding instantaneous velocities of the LIDAR data points of the subgroup and based on a stored model for the dynamic object, the stored model describing geometric features of the dynamic object.

3. The method of claim 2 , wherein the stored model is a three-dimensional model of the dynamic object.

4. The method of claim 2 , further comprising:
selecting the stored model, from multiple candidate stored models, based on determining that sensor data, from the vehicle, corresponds most closely to the stored model.

5. The method of claim 4 , wherein the sensor data comprises the group of LIDAR data points.

6. The method of claim 2 , wherein determining the at least one instantaneous vehicle characteristic is based on the plurality of the corresponding instantaneous velocities of the LIDAR data points of the subgroup and based on the stored model for the dynamic object comprises:
determining a velocity differential based on comparison of a first set velocity magnitude and a second set velocity magnitude, wherein the first set velocity magnitude is based on a first set of the corresponding instantaneous velocities of the LIDAR data points of the subgroup, and wherein the second set velocity magnitude is based on a second set of the corresponding instantaneous velocities of the LIDAR data points of the subgroup; and
converting the velocity differential to the at least one instantaneous vehicle characteristic based on the stored model for the dynamic object.

7. The method of claim 2 , wherein determining the at least one instantaneous vehicle characteristic based on the plurality of the corresponding instantaneous velocities of the LIDAR data points of the subgroup and based on the stored model for the dynamic object comprises:
simulating, using a simulator, movement of the stored model with application, to the stored model, of the corresponding instantaneous velocities of the LIDAR data points of the subgroup.

8. The method of claim 1 , wherein determining the at least one instantaneous vehicle characteristic of the dynamic object based on the corresponding instantaneous velocities of the plurality of the LIDAR data points of the subgroup comprises:
determining a first set of one or more of the plurality of the LIDAR data points of the subgroup;
determining a second set of one or more of the plurality of the LIDAR data points of the subgroup based on the second set being spatially offset from the first set; and
determining the at least one instantaneous vehicle characteristic based on comparison of one or more of the corresponding instantaneous velocities of the first set to one or more of the corresponding instantaneous velocities of the second set.

9. The method of claim 1 , wherein the at least one instantaneous vehicle characteristic of the dynamic object is a lower bound yaw rate of the dynamic object, and further comprising determining an additional instantaneous vehicle characteristic of the dynamic object, that is an upper bound yaw rate, based on comparison of the first set to the second set.

10. The method of claim 1 , wherein the at least one instantaneous vehicle characteristic includes a yaw rate and a yaw direction, and wherein determining the at least one instantaneous vehicle characteristic of the dynamic object based on the corresponding instantaneous velocities of the plurality of the LIDAR data points of the subgroup comprises:
processing the corresponding instantaneous velocities of the plurality of the LIDAR data points of the subgroup using a trained neural network model;
generating, based on processing of the LIDAR data points of the group using the trained neural network model, an output that indicates the yaw rate and the yaw direction; and
determining the yaw rate and the yaw direction based on the output.

11. The method of claim 1 , wherein the at least one instantaneous vehicle characteristic of the dynamic object is a velocity differential that is indicative of yaw rate, and wherein adapting autonomous control of the vehicle based on the determined at least one instantaneous vehicle characteristic of the dynamic object comprises adapting a velocity of the vehicle and/or a direction of the vehicle based on the velocity differential exceeding a threshold.

12. The method of claim 1 , wherein adapting autonomous control of the vehicle based on the determined at least one instantaneous vehicle characteristic of the dynamic object comprises:
determining at least one candidate trajectory of the dynamic object based on the determined at least one instantaneous vehicle characteristic; and
adapting autonomous control of the vehicle based on the at least one candidate trajectory.

13. The method of claim 12 , wherein the dynamic object is an additional vehicle and wherein adapting autonomous control of the vehicle based on the at least one candidate trajectory comprises performing autonomous evasive steering.

14. The method of claim 1 , wherein the LIDAR component is a LIDAR monopulse component and wherein the corresponding sensing events of the LIDAR component each comprise a first receiver sensing event at a first coherent receiver of the LIDAR monopulse component and a second receiver sensing event at a second coherent receiver of the LIDAR monopulse component, the first coherent receiver being positionally offset from the second coherent receiver.

15. The method of claim 14 , wherein the LIDAR data points of the group are super-resolution LIDAR data points generated based on combining the first receiver sensing events and the second receiver sensing events.

16. A method comprising:
receiving, from a phase coherent Light Detection and Ranging (LIDAR) monopulse component of a vehicle, LIDAR data capturing an environment of the vehicle,
the LIDAR data indicating, for each of a plurality of points in the environment of the vehicle, at least one corresponding instantaneous range and at least one corresponding instantaneous velocity based on a corresponding sensing event of a LIDAR monopulse component, the corresponding sensing events of the LIDAR component each comprising a first receiver sensing event at a first coherent receiver of t

Assignees

• Aurora Operations Inc

Inventors

• Smith Warren
• Eade Ethan
• Anderson Sterling J
• Bagnell James Andrew
• Nabbe Bartholomeus C
• Urmson Christopher Paul

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• B60W40/00

  Estimation or calculation of {non-directly measurable} driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, {e.g. by using mathematical models} · CPC title

• B60W40/10

  related to vehicle motion · CPC title

• B60W60/001 Primary

  Planning or execution of driving tasks · CPC title