Qualcomm Incorporated

Nvidia Corporation

Zhaoyang Lv

Jan Kautz

Kihwan Kim

Deqing Sun

Alejandro Jose Troccoli

Aliakbar Aghamohammadi

Amirhossein Tamjidi

A neural network model receives color data for a sequence of images corresponding to a dynamic scene in three-dimensional (3D) space. Motion of objects in the image sequence results from a combination of a dynamic camera orientation and motion or a change in the shape of an object in the 3D space. The neural network model generates two components that are used to produce a 3D motion field representing the dynamic (non-rigid) part of the scene. The two components are information identifying dynamic and static portions of each image and the camera orientation. The dynamic portions of each image contain motion in the 3D space that is independent of the camera orientation. In other words, the motion in the 3D space (estimated 3D scene flow data) is separated from the motion of the camera.

Learning rigidity of dynamic scenes for three-dimensional scene flow estimation

Aspects of the disclosure are related to a method, apparatus and system for joint motion planning and trajectory estimation, comprising: determining a cost function to describe system kinematics comprising trajectories, speeds, and accelerations of a host vehicle and of one or more other vehicles for each possible intention of the host vehicle and of the other vehicles, wherein the trajectories are described with spline functions; and determining jointly the trajectories of the host vehicle and of the other vehicles.

Holistic planning with multiple intentions for self-driving cars

Aspects of the disclosure are related to a method, apparatus, and system for planning a motion for a first vehicle, comprising: estimating past states of an observed second vehicle based on sensor inputs; predicting a future trajectory of the second vehicle based on the estimated past states; planning a future trajectory of the first vehicle based on the predicted future trajectory of the second vehicle and a safety cost function; and driving the first vehicle to follow the planned trajectory.

Motion planning and intention prediction for autonomous driving in highway scenarios via graphical model-based factorization

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Smyrna, GA, United States of America

Zhaoyang Lv