公开(公告)号：US11565709B1

公开(公告)日：2023-01-31

申请号：US16555988

申请日：2019-08-29

Applicant: Zoox, Inc.

Inventor： Timothy Caldwell ,  Jefferson Bradfield Packer ,  William Anthony Silva ,  Rick Zhang ,  Gowtham Garimella

IPC: B60W50/06 ,  B60W50/04 ,  B60W50/00 ,  G05B13/02 ,  G06N3/08 ,  G05B13/04

Abstract: Techniques for generating simulations for evaluating a performance of a controller of an autonomous vehicle are described. A computing system may evaluate the performance of the controller to navigate the simulation and respond to actions of one or more objects (e.g., other vehicles, bicyclists, pedestrians, etc.) in a simulation. Actions of the objects in the simulation may be controlled by the computing system (e.g., by an artificial intelligence) and/or one or more users inputting object controls, such as via a user interface. The computing system may calculate performance metrics associated with the actions performed by the vehicle in the simulation as directed by the autonomous controller. The computing system may utilize the performance metrics to verify parameters of the autonomous controller (e.g., validate the autonomous controller) and/or to train the autonomous controller utilizing machine learning techniques to bias toward preferred actions.

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.

公开(公告)号：US20220274625A1

公开(公告)日：2022-09-01

申请号：US17187170

申请日：2021-02-26

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Andres Guillermo Morales Morales

IPC: B60W60/00 ,  G06N3/08 ,  G06N3/04 ,  B60W30/095 ,  B60W50/00 ,  B60W30/09

Abstract: Techniques are discussed herein for generating and using graph neural networks (GNNs) including vectorized representations of map elements and entities within the environment of an autonomous vehicle. Various techniques may include vectorizing map data into representations of map elements, and object data representing entities in the environment of the autonomous vehicle. In some examples, the autonomous vehicle may generate and/or use a GNN representing the environment, including nodes stored as vectorized representations of map elements and entities, and edge features including the relative position and relative yaw between the objects. Machine-learning inference operations may be executed on the GNN, and the node and edge data may be extracted and decoded to predict future states of the entities in the environment.

公开(公告)号：US20210347377A1

公开(公告)日：2021-11-11

申请号：US16870083

申请日：2020-05-08

Applicant: Zoox, Inc.

Inventor： Kenneth Michael Siebert ,  Gowtham Garimella ,  Samir Parikh

IPC: B60W60/00 ,  G05D1/00 ,  G05D1/02 ,  G06N20/00 ,  G06N5/04

Abstract: Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include inputting data into a model and receiving an output from the model representing a discretized representation. The discretized representation may be associated with a probability of an object reaching a location in the environment at a future time. A vehicle computing system may determine a trajectory and a weight associated with the trajectory using the discretized representation and the probability. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory and the weight output by the vehicle computing system.

公开(公告)号：US11023749B2

公开(公告)日：2021-06-01

申请号：US16504147

申请日：2019-07-05

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Marin Kobilarov ,  Andres Guillermo Morales Morales ,  Kai Zhenyu Wang

IPC: G06K9/00 ,  G06K9/62 ,  G08G1/04 ,  G08G1/01 ,  G06T11/60 ,  G08G1/052 ,  G08G1/056 ,  G05B13/02

Abstract: Techniques for determining predictions on a top-down representation of an environment based on vehicle action(s) are discussed herein. Sensors of a first vehicle (such as an autonomous vehicle) can capture sensor data of an environment, which may include object(s) separate from the first vehicle (e.g., a vehicle or a pedestrian). A multi-channel image representing a top-down view of the object(s) and the environment can be generated based on the sensor data, map data, and/or action data. Environmental data (object extents, velocities, lane positions, crosswalks, etc.) can be encoded in the image. Action data can represent a target lane, trajectory, etc. of the first vehicle. Multiple images can be generated representing the environment over time and input into a prediction system configured to output prediction probabilities associated with possible locations of the object(s) in the future, which may be based on the actions of the autonomous vehicle.

公开(公告)号：US10829149B1

公开(公告)日：2020-11-10

申请号：US15841260

申请日：2017-12-13

Applicant: Zoox, Inc.

Inventor： Gowtham Garimella ,  Joseph Funke ,  Chuang Wang ,  Marin Kobilarov

IPC: B62D6/00 ,  G05D1/02 ,  G05D1/00 ,  B62D5/04

Abstract: Model-based control of dynamical systems typically requires accurate domain-specific knowledge and specifications system components. Generally, steering actuator dynamics can be difficult to model due to, for example, an integrated power steering control module, proprietary black box controls, etc. Further, it is difficult to capture the complex interplay of non-linear interactions, such as power steering, tire forces, etc. with sufficient accuracy. To overcome this limitation, a recurring neural network can be employed to model the steering dynamics of an autonomous vehicle. The resulting model can be used to generate feedforward steering commands for embedded control. Such a neural network model can be automatically generated with less domain-specific knowledge, can predict steering dynamics more accurately, and perform comparably to a high-fidelity first principle model when used for controlling the steering system of a self-driving vehicle.