Nvidia Corporation

Microsoft Technology Licensing, LLC

Alexey Kamenev

Nikolai Smolyanskiy

David Nister

Ruchi Bhargava

Stan Birchfield

Sangmin Oh

Stanley Thomas Birchfield

Ibrahim Eden

Ke Chen

Joachim Pehserl

Tilman Wekel

Ryan Oldja

Rotem Aviv

Jeffrey David Smith

Ollin Boer Bohan

Urs A Muller

Alexander Dadiomov

Xinhua Ji

Julia C Ng

Yizhou Wang

Eric Ledoux

Birgit Henke

Mayank Agarwal

Ishwar Kulkarni

Joshua John Bialkowski

Hon Leung Lee

Miguel Sainz Serra

Matthew L Jacobs

Zhenyi Zhang

Anton Vorontsov

Seungho Lee

Raul Correal Tezanos

Fangkai Yang

James Rapp

Daniel Griffing

Ben Nesson

Drake A Campbell

Paulo Cesar Sales Janotti

Alperen Degirmenci

Hermes Lanker

Jared Robert Van Leeuwen

A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

Deep neural network for segmentation of road scenes and animate object instances for autonomous driving applications

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

Stereo depth estimation using deep neural networks

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

A method, computer readable medium, and system are disclosed for performing autonomous path navigation using deep neural networks. The method includes the steps of receiving image data at a deep neural network (DNN), determining, by the DNN, both an orientation of a vehicle with respect to a path and a lateral position of the vehicle with respect to the path, utilizing the image data, and controlling a location of the vehicle, utilizing the orientation of the vehicle with respect to the path and the lateral position of the vehicle with respect to the path.

Performing autonomous path navigation using deep neural networks

The present invention extends to methods, systems, and computer program products for linking diagnostic visualizations to regions of application code. Diagnostic visualizations emitted during execution of an application are displayed. The diagnostic visualizations partially represent the abstract objective of the application (e.g., as envisioned by a developer). Diagnostic data for at least one of a plurality of components is displayed. The diagnostic data indicates the performance of the at least one of the plurality of components during execution of the application. The displayed one or more diagnostic visualizations and the displayed diagnostic data is correlated to link the one or more diagnostic visualizations to the at least one of the plurality of components. Linking the one or more diagnostic visualizations to the at least one of the plurality of components can better indicate how the application's behavior reconciles the abstract objective.

Linking diagnostic visualizations to application code

Growing community of inventors

Bellevue, WA, United States of America

Alexey Kamenev