Ford Global Technolgoies, LLC

Disney Enterprises, Inc.

Volkswagen Group of America Investments, LLC

George Peter Kenneth Carr

Andrew Thomas Hartnett

Brett Browning

Michael Pacilio

Nikolai Popov

Neal Andrew Seegmiller

Mark Ollis

Constantin Savtchenko

Christopher Cunningham

Basel Alghanem

Yisong Yue

Russell Schloss

Benjamin D Ballard

Peter W Rander

Shawn Cook

Christian Höeppner

Hoang M Le

Disclosed herein are system, method, and computer program product aspects for testing autonomous vehicle intent using protolanes. For example, the method includes selecting a first lane segment of a roadway within a geonet, wherein the geonet comprises a plurality of lane segments. It is determined that the first lane segment corresponds to a protolane of a set of protolanes, wherein the protolane is associated with one or more additional lane segments of the plurality of lane segments. The protolane is associated with the first lane segment based on the determination that the first lane segment corresponds to the protolane.

Protolanes for testing autonomous vehicle intent

Disclosed herein are system, method, and computer program product embodiments for clustering lane segments of a roadway in order to improve and simplify autonomous vehicle behavior testing. The approaches disclosed herein provide a hybrid methodology of dividing lane segments into hard features and soft features, and using a metric learning model trained in a supervised process on the entirety of lane segment features to cluster the lane segments based on the soft features. These clustered lane segments can then be assigned to what is termed as protolanes, where a single set of tests applied to a given protolane is considered valid across all of the lane segments assigned to the protolane.

Lane segment clustering using hybrid distance metrics

Disclosed herein are system, method, and computer program product aspects for validating protolanes. For example, the method includes determining a protolane level flowchart from one or more flowcharts corresponding to one or more gates of a protolane selected from a set of protolanes of a geonet, wherein the one or more flowcharts correspond to conditions that can be encountered by an actor approaching the corresponding one or more gates, and wherein the geonet comprises a plurality of lane segments associated with the set of protolanes. A graph through the protolane level flowchart is generated that corresponds to actions of an actor. Reasoning of the actor is validated by testing the actions of the actor against the graph through the protolane level flowchart.

Validating protolanes with actor behavior flowcharts

One embodiment provides a method, comprising: training, using deep imitation learning, a neural network associated with a predetermined ghosting model to predict player movements for at least one player during at least one sequence in a game; receiving, at an information handling device, tracking data associated with a player movement path for at least one player during the at least one sequence; analyzing, using a processor, the tracking data to determine at least one feature associated with the at least one player at a plurality of predetermined time points during the at least one sequence; and determining, using the predetermined ghosting model and the at least one feature, a ghosted movement path for the at least one player beginning from one of the plurality of predetermined time points. Other aspects are described and claimed.

Data-driven ghosting using deep imitation learning

Systems and methods are described for measuring velocity of an object detected by a light detection and ranging (lidar) system. According to some aspects a method may include receiving a lidar dataset generated by the lidar system, transforming the lidar dataset into a first layer dataset and a second layer dataset, and converting the first layer dataset into a first image and the second layer dataset into a second image. The method may also include performing a feature detection operation that identifies at least one feature in the first image and the same feature in the second image, locating a first location of the feature in the first image and a second location of the feature in the second image, and generating a velocity estimate of the feature based on a difference between the first location and the second location and a difference between the different time intervals.

Systems and method for lidar grid velocity estimation

Growing community of inventors

Allison Park, PA, United States of America

George Peter Kenneth Carr