Uatc, LLC

Disney Enterprises, Inc.

Nvidia Corporation

Aurora Operations, Inc.

Tian Lan

Galen Clark Haynes

Alexander David Styler

Leonid Sigal

Anthony Joseph Stentz

Nemanja Djuric

Jeff Schneider

Micol Marchetti-Bowick

Vladan Radosavljevic

Tsung-Han Lin

Tzu-Kuo Huang

Baoan Liu

Neil Traft

Michail Raptis

Kezhao Chen

Ian Dewancker

Huahua Wang

Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with a first object and one or more second objects within a surrounding environment of an autonomous vehicle. The operations include determining an interaction between the first object and the one or more second objects based at least in part on the data. The operations include determining one or more predicted trajectories of the first object within the surrounding environment based at least in part on the interaction between the first object and the one or more second objects. The operations include outputting data indicative of the one or more predicted trajectories of the first object.

Object interaction prediction systems and methods for autonomous vehicles

In various examples, operations include obtaining, from a machine learning model, feature classifications that correspond to features of objects depicted in images of a geographical area in which the images are provided to the machine learning model. The operations may also include annotating the images with three-dimensional representations that are based on the obtained feature classifications. Further, the operations may include generating map data corresponding to the geographical area based on the annotated images.

High-definition maps and localization for road vehicles

The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.

Machine learning for predicting locations of objects perceived by autonomous vehicles

The disclosure provides an approach for recognizing and analyzing activities. In one embodiment, a learning application trains parameters of a hierarchical model which represents human (or object) activity at multiple levels of detail. Higher levels of detail may consider more context, and vice versa. Further, learning may be optimized for a user-preferred type of inference by adjusting a learning criterion. An inference application may use the trained model to answer queries about variable(s) at any level of detail. In one embodiment, the inference application may determine scores for each possible value of the query variable by finding the best hierarchical event representation that maximizes a scoring function while fixing the value of the query variable to its possible values. Here, the inference application may approximately determine the best hierarchical event representation by iteratively optimizing one level-of-detail variable at a time while fixing other level-of-detail variables, until convergence.

Hierarchical model for human activity recognition

The disclosure provides an approach for detecting objects in images. An object detection application receives a set of training images with object annotations. Given these training images, the object detection application generates semantic labeling for object detections, where the labeling includes lower-level subcategories and higher-level visual composites. In one embodiment, the object detection application identifies subcategories using an exemplar support vector machine (SVM) based clustering approach. Identified subcategories are used to initialize mixture components in mixture models which the object detection application trains in a latent SVM framework, thereby learning a number of subcategory classifiers that produce, for any given image, a set of candidate windows and associated subcategory labels. In addition, the object detection application learns a structured model for object detection that captures interactions among object subcategories and identifies discriminative visual composites, using subcategory labels and spatial relationships between subcategory labels to reason about object interactions.

Multi-level framework for object detection

Growing community of inventors

Pittsburgh, PA, United States of America

Tian Lan