Vehicle trajectory tree structure including learned trajectories

Patent metadata

Field	Value
Publication number	US-12454285-B2
Application number	US-202318204097-A
Country	US
Kind code	B2
Filing date	May 31, 2023
Priority date	May 31, 2023
Publication date	Oct 28, 2025
Grant date	Oct 28, 2025

Abstract

Techniques for generating a tree structure based on multiple machine-learned trajectories are described herein. A planning component (“ML system”) within a vehicle may receive and encode various types of sensor and/or vehicle data. The ML system can provide the encoded data as input to multiple machine-learning models (“ML models”), each of which may be trained to output a unique candidate trajectory for the vehicle follow. In some examples, each ML model may be trained to output a unique type of learned trajectory that causes the vehicle to perform a certain type of action. Using the learned candidate trajectories, the ML system may generate a tree structure that includes some or all of the candidate trajectories. The vehicle may determine a control trajectory based on the generation and traversal of the tree structure using a tree search algorithm, and may follow the control trajectory within the environment.

First claim

What is claimed is:

1 . A system comprising:
one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising:
receiving, from a sensor associated with an autonomous vehicle, sensor data;
determining, using a first machine learned model and based at least in part on the sensor data, an encoding;
inputting the encoding into a second machine-learning model;
receiving, from the second machine-learning model, a first candidate trajectory of a first type;
receiving, from the second machine-learning model, a second candidate trajectory having a second type that is different from the first type;
determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold;
generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure;
determining a control trajectory for the autonomous vehicle, based at least in part on the tree structure; and
controlling the autonomous vehicle based at least in part on the control trajectory.

2 . The system of claim 1 , wherein determining the encoding is further based at least in part on:
a state of the autonomous vehicle, a characteristic of an object within an environment, or a feature of a surface of a road.

3 . The system of claim 1 , the operations further comprising:
determining, based on a heuristic, a third candidate trajectory, wherein generating the tree structure is further based at least in part on the third candidate trajectory.

4 . The system of claim 1 , wherein the second machine-learning model is trained such that a difference between a first output trajectory and a second output trajectory meets or exceeds a threshold difference.

5 . The system of claim 1 , wherein the tree structure includes a plurality of nodes that are associated with the first candidate trajectory or the second candidate trajectory.

6 . One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising:
receiving, from a sensor associated with a vehicle, sensor data; determining, based at least in part on the sensor data, a representation of an environment; inputting the representation into a machine-learning model; determining, using the machine-learning model and based at least in part on the representation, a first candidate trajectory for the vehicle; determining, using the machine-learning model and based at least in part on the representation, a second candidate trajectory for the vehicle, wherein the second candidate trajectory differs from the first candidate trajectory by at least a threshold difference; determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold; generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure; and determining a control trajectory for the vehicle, based at least in part on the tree structure.

7 . The one or more non-transitory computer-readable media of claim 6 , wherein determining the representation is further based at least in part on one or more of:
a state of the vehicle, a characteristic of an object within the environment, or a feature of a surface of a road.

8 . The one or more non-transitory computer-readable media of claim 6 , the operations further comprising:
determining, based at least in part on a heuristic, a third candidate trajectory, wherein generating the tree structure is further based at least in part on the third candidate trajectory.

9 . The one or more non-transitory computer-readable media of claim 6 , wherein the first candidate trajectory is of a first type and the second candidate trajectory is of a second type, and wherein one or more of the first type or the second type are associated with one or more of:
a lane change, a modified velocity, a modified acceleration, a modified pose within a current lane, or remaining in the current lane.

10 . The one or more non-transitory computer-readable media of claim 6 , wherein the control trajectory comprises of a first portion of the first candidate trajectory and a second portion of the second candidate trajectory.

11 . The one or more non-transitory computer-readable media of claim 6 , wherein the operations further comprising:
controlling the vehicle based at least in part on the control trajectory.

12 . The one or more non-transitory computer-readable media of claim 6 , wherein determining that the first candidate trajectory differs from the second candidate trajectory is based at least in part on comparing one or more of:
a first steering angle of the first candidate trajectory and a second steering angle of the second candidate trajectory, a first velocity of the first candidate trajectory and a second velocity of the second candidate trajectory, or a first acceleration of the first candidate trajectory and a second acceleration of the second candidate trajectory.

13 . The one or more non-transitory computer-readable media of claim 6 , wherein the tree structure includes a plurality of nodes that are associated with the first candidate trajectory or the second candidate trajectory.

14 . A method comprising:
receiving, from a sensor associated with a vehicle, sensor data; determining, based at least in part on the sensor data, a representation of an environment; inputting the representation into a machine-learning model; determining, using the machine-learning model and based at least in part on the representation, a first candidate trajectory for the vehicle; determining, using the machine-learning model and based at least in part on the representation, a second candidate trajectory for the vehicle, wherein the second candidate trajectory differs from the first candidate trajectory by at least a threshold difference; determining that both the first candidate trajectory and the second candidate trajectory meet or exceed a probability threshold; generating, based at least in part on the first candidate trajectory and the second candidate trajectory meeting or exceeding the probability threshold, a tree structure; and determining a control trajectory for the vehicle, based at least in part on the tree structure.

15 . The method of claim 14 , wherein determining the representation is further based at least in part on one or more of:
a state of the vehicle, a characteristic of an object within the environment, or a feature of a surface of a road.

16 . The method of claim 14 , further comprising:
determining, based at least in part on a heuristic, a third candidate trajectory, wherein generating the tree structure is further based at least in part on the third candidate trajectory.

17 . The method of claim 14 , wherein the first candidate trajectory is associated with a first type and the second candidate trajectory is associated with a second type, and wherein one or more of the first type or the second type are associated with one or more of:
a lane change, a modified velocity, a modified acceleration, a modified pose within a current lane, or remaining in the current lane.

18 . The met

Assignees

• Zoox Inc

Inventors

• Lee Seungji
• Linscott Gary
• Schleede Peter Scott

Classifications

• B60W40/06

  Road conditions · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• B60W2554/80

  Spatial relation or speed relative to objects · CPC title

• B60W30/18163

  Lane change; Overtaking manoeuvres · CPC title

• B60W60/0011 Primary

  involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles · CPC title