Argo Ai Gmbh

Vannevar Labs, Inc.

Ford Global Technolgoies, LLC

Nathaniel Honka

Daniel Goodman

Nathanial Hartman

Brett Granberg

Eleony Moorhead

Christopher Cunningham

Yuh-Shyang Wang

Damir Mirkovic

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining one or more candidate vehicle trajectories for the autonomous vehicle and context information defining a state of an environment surrounding the autonomous vehicle; assigning class(es) to a scenario specified by the context information and a first candidate vehicle trajectory; generating a first quality score for the first candidate vehicle trajectory using scoring function(s) selected based on the assigned class(es); select a candidate vehicle trajectory based on the first quality score associated with the first candidate vehicle trajectory and second quality score(s) associated with at least one second candidate vehicle trajectory; and causing the autonomous vehicle to perform autonomous driving operations using the selected candidate vehicle trajectory.

Systems and methods for scenario dependent trajectory scoring

System and methods for causing movement of an autonomous vehicle. The methods comprise: determining candidate trajectories of the autonomous vehicle using candidate actions forecasted for a virtual doppelganger and reactive actions predicted for a non-Ego actor that is located in proximity to the autonomous vehicle; selecting first candidate trajectories of the candidate trajectories in which the autonomous vehicle has an influence on a response of the non-Ego actor; using the first candidate trajectories for the autonomous vehicle to refine the reactive actions that were predicted for the non-Ego actor; using the reactive actions that were refined to select one of the first candidate trajectories as a selected trajectory for the autonomous vehicle to follow; and causing an automation subsystem of the autonomous vehicle to move the autonomous vehicle along the selected trajectory.

Method and system for forecasting reactions of other road users in autonomous driving

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural network-based optical character recognition. An embodiment of the system may generate a set of bounding boxes based on reshaped image portions that correspond to image data of a source image. The system may merge any intersecting bounding boxes into a merged bounding box to generate a set of merged bounding boxes indicative of image data portions that likely portray one or more words. Each merged bounding box may be fed by the system into a neural network to identify one or more words of the source image represented in the respective merged bounding box. The one or more identified words may be displayed by the system according to a standardized font and a confidence score.

Neural network-based optical character recognition

A method of determining a trajectory for an autonomous vehicle (AV) is disclosed. When the AV's on-board system detects another actor near the AV, it will generate a virtual doppelganger that is co-located with the autonomous vehicle. The system will predict possible modalities for the other actor and, for each of the modalities, one or more possible trajectories for the other actor. The system then forecast candidate actions of the virtual doppelganger, each of which corresponds to one or more of the possible trajectories of the other actor. The computing system will then determine candidate trajectories of the autonomous vehicle, and it will select one of the candidate trajectories as a selected trajectory for the autonomous vehicle to follow.

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Mountain View, CA, United States of America

Nathaniel Honka