公开(公告)号：US12271204B1

公开(公告)日：2025-04-08

申请号：US17081203

申请日：2020-10-27

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Kai Zhenyu Wang

IPC: G06N20/00 ,  G05D1/00 ,  G06V20/58

Abstract: Techniques are discussed for predicting an occupancy of visible region of an environment. For instance, a vehicle may generate sensor data representing an environment. The vehicle may then analyze the sensor data to determine an occluded region of the environment a visible region of the environment. Additionally, the vehicle may determine at least one prediction probability associated with occupancy of the visible region over a future period of time. In some instances, the vehicle determines the at least one prediction probability by inputting data representing at least the occluded region and the visible region into a machine learned model and receiving the at least one prediction probability from the machine learned model. Using the at least one prediction probability, the vehicle may then determine and perform one or more actions.

公开(公告)号：US12084087B2

公开(公告)日：2024-09-10

申请号：US17535418

申请日：2021-11-24

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Andres Guillermo Morales Morales ,  Ethan Miller Pronovost ,  Kai Zhenyu Wang ,  Xiaosi Zeng

IPC: B60W60/00 ,  G06N3/02

CPC classification number: B60W60/0027 ,  G06N3/02 ,  B60W2554/402 ,  B60W2554/4041 ,  B60W2554/4042 ,  B60W2554/4043 ,  B60W2554/4045 ,  B60W2554/4046 ,  B60W2555/60 ,  B60W2556/40

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future that meet a criterion, allowing for more efficient sampling. A predicted position of the object in the future may be determined by sampling from the distribution.

公开(公告)号：US12080044B2

公开(公告)日：2024-09-03

申请号：US17535396

申请日：2021-11-24

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Andres Guillermo Morales Morales ,  Ethan Miller Pronovost ,  Kai Zhenyu Wang ,  Xiaosi Zeng

IPC: G06V10/44 ,  B60W60/00 ,  G06F16/901 ,  G06V10/422 ,  G06V10/764

CPC classification number: G06V10/454 ,  B60W60/0027 ,  G06F16/9024 ,  G06V10/422 ,  G06V10/764

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.

公开(公告)号：US11966230B1

公开(公告)日：2024-04-23

申请号：US17125388

申请日：2020-12-17

Applicant: Zoox, Inc.

Inventor： Greg Woelki ,  Kai Zhenyu Wang ,  Bertrand Robert Douillard ,  Michael Haggblade ,  James William Vaisey Philbin

IPC: G05D1/02 ,  B60W60/00 ,  G05D1/00 ,  G06N7/01 ,  G06N20/00 ,  G06V20/58

CPC classification number: G05D1/0221 ,  B60W60/0027 ,  B60W60/005 ,  G05D1/0214 ,  G05D1/0231 ,  G05D1/0276 ,  G06N7/01 ,  G06N20/00 ,  G06V20/58 ,  B60W2420/42 ,  B60W2554/4026 ,  B60W2554/4029 ,  B60W2554/404 ,  B60W2556/10 ,  B60W2556/45 ,  G05D2201/0213

Abstract: Techniques for determining a prediction probability associated with a disengagement event are discussed herein. A first prediction probability can include a probability that a safety driver associated with a vehicle (such as an autonomous vehicle) may assume control over the vehicle. A second prediction probability can include a probability that an object in an environment is associated the disengagement event. Sensor data can be captured and represented as a top-down representation of the environment. The top-down representation can be input to a machine learned model trained to output prediction probabilities associated with a disengagement event. The vehicle can be controlled based the prediction probability and/or the interacting object probability.

公开(公告)号：US20230162470A1

公开(公告)日：2023-05-25

申请号：US17535396

申请日：2021-11-24

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Andres Guillermo Morales Morales ,  Ethan Miller Pronovost ,  Kai Zhenyu Wang ,  Xiaosi Zeng

IPC: G06V10/44 ,  G06V10/422 ,  B60W60/00 ,  G06F16/901 ,  G06V10/764

CPC classification number: G06V10/454 ,  G06V10/422 ,  B60W60/0027 ,  G06F16/9024 ,  G06V10/764

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a distribution of predicted positions for the object in the future. A predicted position of the object at a subsequent timestep may be determined by sampling from the distribution of predicted positions according to various sampling strategies. Alternatively, the predicted position of the object may be overwritten using a candidate position of the object.

公开(公告)号：US20200272148A1

公开(公告)日：2020-08-27

申请号：US16282201

申请日：2019-02-21

Applicant: Zoox, Inc.

Inventor： Vasiliy Karasev ,  Tencia Lee ,  James William Vaisey Philbin ,  Sarah Tariq ,  Kai Zhenyu Wang

IPC: G05D1/00 ,  G01S17/50 ,  G01S13/58 ,  G05D1/02 ,  G06N20/10

Abstract: Techniques for determining and/or predicting a trajectory of an object by using the appearance of the object, as captured in an image, are discussed herein. Image data, sensor data, and/or a predicted trajectory of the object (e.g., a pedestrian, animal, and the like) may be used to train a machine learning model that can subsequently be provided to, and used by, an autonomous vehicle for operation and navigation. In some implementations, predicted trajectories may be compared to actual trajectories and such comparisons are used as training data for machine learning.

公开(公告)号：US12296858B2

公开(公告)日：2025-05-13

申请号：US17529803

申请日：2021-11-18

Applicant: Zoox, Inc.

Inventor： Andres Guillermo Morales Morales ,  Samir Parikh ,  Kai Zhenyu Wang

IPC: B60W60/00

Abstract: Techniques for determining a response of a simulated vehicle to a simulated object in a simulation are discussed herein. Log data captured by a physical vehicle in an environment can be received. Object data representing an object in the log data can be used to instantiate a simulated object in a simulation to determine a response of a simulated vehicle to the simulated object. Additionally, one or more trajectory segments in a trajectory library representing the log data can be determined and instantiated as a trajectory of the simulated object in order to increase the accuracy and realism of the simulation.

公开(公告)号：US12103561B2

公开(公告)日：2024-10-01

申请号：US17966037

申请日：2022-10-14

Applicant: Zoox, Inc.

Inventor： Pengfei Duan ,  James William Vaisey Philbin ,  Cooper Stokes Sloan ,  Sarah Tariq ,  Feng Tian ,  Chuang Wang ,  Kai Zhenyu Wang ,  Yi Xu

IPC: B60W60/00 ,  G01C21/32 ,  G01S17/86 ,  G05D1/00

CPC classification number: B60W60/0025 ,  B60W60/0027 ,  G01C21/32 ,  G01S17/86 ,  G05D1/0088 ,  G05D1/0214 ,  G05D1/0223 ,  G05D1/0274 ,  B60W2420/403 ,  B60W2420/408 ,  B60W2552/05 ,  B60W2552/53 ,  B60W2555/60

Abstract: Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

公开(公告)号：US12065171B2

公开(公告)日：2024-08-20

申请号：US17535357

申请日：2021-11-24

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Andres Guillermo Morales Morales ,  Ethan Miller Pronovost ,  Kai Zhenyu Wang ,  Xiaosi Zeng

IPC: G05D1/02 ,  B60W60/00 ,  G05D1/00 ,  G06N3/04 ,  G06N3/08 ,  G06N5/04

CPC classification number: B60W60/0027 ,  G05D1/0274 ,  G06N3/08 ,  G06N5/04 ,  B60W2552/53 ,  B60W2554/20 ,  B60W2554/402 ,  B60W2554/4041 ,  B60W2554/4042 ,  B60W2554/4043 ,  B60W2554/80 ,  B60W2555/20 ,  B60W2555/60

Abstract: Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a predicted position of the object at a subsequent timestep. Further, a predicted trajectory of the object may be determined using predicted positions of the object at various timesteps.

公开(公告)号：US20220314993A1

公开(公告)日：2022-10-06

申请号：US17218051

申请日：2021-03-30

Applicant: Zoox, Inc.

Inventor： Gerrit Bagschik ,  Andrew Scott Crego ,  Gowtham Garimella ,  Michael Haggblade ,  Andraz Kavalar ,  Kai Zhenyu Wang

IPC: B60W30/18 ,  B60W50/14 ,  G06N3/08 ,  G06N3/04

Abstract: Techniques for top-down scene discrimination are discussed. A system receives scene data associated with an environment proximate a vehicle. The scene data is input to a convolutional neural network (CNN) discriminator trained using a generator and a classification of the output of the CNN discriminator. The CNN discriminator generates an indication of whether the scene data is a generated scene or a captured scene. If the scene data is data generated scene, the system generates a caution notification indicating that a current environmental situation is different from any previous situations. Additionally, the caution notification is communicated to at least one of a vehicle system or a remote vehicle monitoring system.