Nvidia Corporation

Ollin Boer Bohan

Nikolai Smolyanskiy

David Nister

Alexey Kamenev

Rotem Aviv

Urs A Muller

Ruchi Bhargava

Joachim Pehserl

Julia C Ng

Yizhou Wang

Birgit Henke

Joshua John Bialkowski

Ishwar Kulkarni

Hon Leung Lee

Miguel Sainz Serra

Zhenyi Zhang

Raul Correal Tezanos

Fangkai Yang

Anton Vorontsov

Seungho Lee

Jens Christian Bo Joergensen

Alperen Degirmenci

Hermes Lanker

In various examples, systems and methods of the present disclosure detect and/or track objects in an environment using projection images generated from LiDAR. For example, a machine learning model—such as a deep neural network (DNN)—may be used to compute a motion mask indicative of motion corresponding to points representing objects in an environment. Various input channels may be provided as input to the machine learning model to compute a motion mask. One or more comparison images may be generated based on comparing depth values projected from a current range image to a coordinate space of a previous range image to depth values of the previous range image. The machine learning model may use the one or more projection images, the one or more comparison images, and/or the one or more range images to compute a motion mask and/or a motion vector output representation.

Dynamic object detection using LiDAR data for autonomous machine systems and applications

In various examples, past location information corresponding to actors in an environment and map information may be applied to a deep neural network (DNN)—such as a recurrent neural network (RNN)—trained to compute information corresponding to future trajectories of the actors. The output of the DNN may include, for each future time slice the DNN is trained to predict, a confidence map representing a confidence for each pixel that an actor is present and a vector field representing locations of actors in confidence maps for prior time slices. The vector fields may thus be used to track an object through confidence maps for each future time slice to generate a predicted future trajectory for each actor. The predicted future trajectories, in addition to tracked past trajectories, may be used to generate full trajectories for the actors that may aid an ego-vehicle in navigating the environment.

Future trajectory predictions in multi-actor environments for autonomous machine

Embodiments of the present disclosure relate to behavior planning for autonomous vehicles. The technology described herein selects a preferred trajectory for an autonomous vehicle based on an evaluation of multiple hypothetical trajectories by different components within a planning system. The various components provide an optimization score for each trajectory according to the priorities of the component and scores from multiple components may form a final optimization score. This scoring system allows the competing priorities (e.g., comfort, minimal travel time, fuel economy) of different components to be considered together. In examples, the trajectory with the best combined score may be selected for implementation. As such, an iterative approach that evaluates various factors may be used to identify an optimal or preferred trajectory for an autonomous vehicle when navigating an environment.

Behavior planning for autonomous vehicles

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Redmond, WA, United States of America

Ollin Boer Bohan