公开(公告)号：US20240199074A1

公开(公告)日：2024-06-20

申请号：US18335028

申请日：2023-06-14

Applicant: NVIDIA Corporation

Inventor： Sushant Veer ,  Karen Leung ,  Ryan Cosner ,  Yuxiao Chen ,  Marco Pavone

IPC: B60W60/00 ,  B60W40/02

CPC classification number: B60W60/0011 ,  B60W40/02

Abstract: Autonomous vehicles (AVs) may need to contend with conflicting traveling rules and the AV controller would need to select the least objectionable control action. A rank-preserving reward function can be applied to trajectories derived from a rule hierarchy. The reward function can be correlated to a robustness vector derived for each trajectory. Thereby the highest ranked rules would result in the highest reward, and the lower ranked rules would result in lower reward. In some aspects, one or more optimizers, such as a stochastic optimizer can be utilized to improve the results of the reward calculation. In some aspects, a sigmoid function can be applied to the calculation to smooth out the step function used to calculate the robustness vector. The preferred trajectory selected using the results from the reward function can be communicated to an AV controller for implementation as a control action.

公开(公告)号：US20240085914A1

公开(公告)日：2024-03-14

申请号：US17942551

申请日：2022-09-12

Applicant: NVIDIA Corporation

Inventor： Sever Ioan Topan ,  Karen Yan Ming Leung ,  Yuxiao Chen ,  Pritish Tupekar ,  Edward Fu Schmerling ,  Hans Jonas Nilsson ,  Michael Cox ,  Marco Pavone

IPC: G05D1/02 ,  G06V20/58

CPC classification number: G05D1/0214 ,  G05D1/0253 ,  G06V20/58 ,  G06V2201/07

Abstract: In various examples, techniques for determining perception zones for object detection are described. For instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. The system may then perform one or more processes using the perception zone. For instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. Additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.

公开(公告)号：US20240160913A1

公开(公告)日：2024-05-16

申请号：US18051114

申请日：2022-10-31

Applicant: NVIDIA Corporation

Inventor： Ryan Cosner ,  Yuxiao Chen ,  Karen Yan Ming Leung ,  Marco Pavone

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: In various examples, learning responsibility allocations for machine interactions is described herein. Systems and methods are disclosed that train a neural network(s) to generate outputs indicating estimated levels of responsibilities associated with interactions between vehicles or machines and other objects (e.g., other vehicles, machines, pedestrians, animals, etc.). In some examples, the neural network(s) is trained using real-world data, such as data representing scenes depicting actual interactions between vehicles and objects and/or parameters (e.g., velocities, positions, directions, etc.) associated with the interactions. Then, in practice, a vehicle (e.g., an autonomous vehicle, a semi-autonomous vehicle, etc.) may use the neural network(s) to generate an output indicating a proposed or estimated level of responsibility associated with an interaction between the vehicle and an object. The vehicle may then use the output to determine one or more controls for the vehicle to use when navigating.

公开(公告)号：US20250058802A1

公开(公告)日：2025-02-20

申请号：US18366202

申请日：2023-08-07

Applicant: NVIDIA Corporation

Inventor： Yuxiao Chen ,  Sushant Veer ,  Peter Karkus ,  Marco Pavone

IPC: B60W60/00

Abstract: In various examples, a gradient-based motion planner evaluates a cost function corresponding to routes for a machine and an obstacle to jointly update the routes. The cost function may include terms to penalize deviation from an initial route predicted for the obstacle and acceleration or jerk for the obstacle. The routes for the machine and the obstacle that are updated may be selected using motion classes that characterize relative motion between a route for the machine and a route for the obstacle. A motion class may be based at least on an angular distance between the machine and the agent and free-end homotopy, where members of the class execute the same relative motion with respect to other agents while being continuously transformable to any other member of the class. The members of the class may have the same start point and different end points.

公开(公告)号：US20240182082A1

公开(公告)日：2024-06-06

申请号：US18354892

申请日：2023-07-19

Applicant: NVIDIA Corporation

Inventor： Yuxiao Chen ,  Peter Karkus ,  Boris Ivanovic ,  Xinshuo Weng ,  Marco Pavone

IPC: B60W60/00

CPC classification number: B60W60/00272 ,  B60W60/0011 ,  B60W2554/4046 ,  B60W2554/80

Abstract: In various examples, policy planning using behavior models for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that determine a policy for navigating a vehicle, such as a semi-autonomous vehicle or an autonomous vehicle (or other machine), where the policy allows for multistage reasoning that leverages future reactive behaviors of one or more other objects. For instance, a first behavior model (e.g., a trajectory tree) may be generated that represents candidate trajectories for the vehicle and one or more second behavior models (e.g., one or more scenario trees) may be generated that respectively represent future behaviors of the other object(s). The first behavior model and the second behavior model(s) may then be processed, such as in a closed-loop simulation based on a realistic data-driven traffic model, to determine the policy for navigating the vehicle.

公开(公告)号：US20230391365A1

公开(公告)日：2023-12-07

申请号：US18114035

申请日：2023-02-24

Applicant: NVIDIA Corporation

Inventor： Boris Ivanovic ,  Danfei Xu ,  Yuxiao Chen ,  Marco Pavone

IPC: B60W60/00 ,  G06N3/045

CPC classification number: B60W60/0011 ,  B60W60/00274 ,  G06N3/045 ,  B60W2554/4041 ,  B60W2554/4044 ,  B60W2554/80 ,  B60W2556/40

Abstract: In various examples, techniques for generating simulations for autonomous machines and applications are described herein. Systems and methods are disclosed that use various models to generate simulations. For instance, a first model(s) may process input data, such as input data representing maps indicating the locations of objects and state history of the objects within the environment, to determine navigation goals for the objects. Additionally, a second model(s) may then process the input data and data representing the navigation goals in order to determine possible trajectories (e.g., action samples) for the objects within the environment. Furthermore, a third model(s) may process the input data to predict trajectories of the objects within the environment. The systems and methods may then use at least the possible trajectories and the predicted trajectories to simulate the motion (e.g., one or more trajectories) of one or more of the objects.