公开(公告)号：US20240017743A1

公开(公告)日：2024-01-18

申请号：US18183582

申请日：2023-03-14

Applicant: NVIDIA Corporation

Inventor： Alec Farid ,  Sushant Veer ,  Boris Ivanovic ,  Karen Leung ,  Marco Pavone

IPC: B60W60/00 ,  B60W30/09

CPC classification number: B60W60/0016 ,  B60W60/00272 ,  B60W30/09

Abstract: In various examples, cost probability distributions corresponding to predicted locations of an object in an environment and potential locations for a machine in the environment and may be evaluated using corresponding observed costs corresponding to the machine and the object. The cost probability distributions may be evaluated based on comparing the observed costs to threshold values, which may be determined based on sampling a predicted cost function. A threshold value may be selected to provide false-positive rate and/or false-negative rate guarantees for anomaly detection. Control operations may be performed based on results of the evaluation of the cost probability distributions. For example, based on the results, a motion planner may reuse a planned trajectory for a future planning cycle (e.g., thereby avoiding re-planning computations) or generate and/or select a new planned trajectory (e.g., based at least on one or more anomalies being detected).

公开(公告)号：US20240010196A1

公开(公告)日：2024-01-11

申请号：US18183566

申请日：2023-03-14

Applicant: NVIDIA Corporation

Inventor： Karen Yan Ming Leung ,  Sushant Veer ,  Edward Fu Schmerling ,  Marco Pavone

IPC: B60W30/095 ,  B60W60/00 ,  B60W30/09

CPC classification number: B60W30/0956 ,  B60W60/0015 ,  B60W30/09 ,  B60W2554/4041

Abstract: In various examples, control policies for controlling agents may be learned from demonstrations capturing joint states of entities navigating through the environment. A control policy may be learned mapping joint states to control actions, where the joint states are between agents, and the control actions are of at least one of the agents. The control policy may be learned to define the mappings as control invariant sets of the joint sates and the control actions. The control policy may be used to determine one or more functions that compute, based at least on a joint state between entities, output indicating a likelihood of collision between the entities operating in accordance with the control policy. Using the output, current and/or potential states of the environment may be evaluated to determine control operations for a machine, such as a vehicle.

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：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.