Tesla, Inc.

Andrej Karpathy

Ashok Kumar Elluswamy

Matthew Bauch

Christopher Payne

Arvind Ramanandan

Joseph Polin

Paul Chen

Elon Reeve Musk

Charles Shieh

Francis Havlak

Nenad Uzunovic

Alexander Hertzberg

Jason Kong

Li Wang

Oktay Arslan

Nicklas Gustafsson

David Seelig

Dhaval Shroff

Kate Park

Christopher Coleman Moore

Stuart Bowers

Ashima Kapur Sud

Paril Jain

James Robert Howard Hakewill

Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.

Generating ground truth for machine learning from time series elements

A processor coupled to memory is configured to receive an identification of a geographical location associated with a target specified by a user remote from a vehicle. A machine learning model is utilized to generate a representation of at least a portion of an environment surrounding the vehicle using sensor data from one or more sensors of the vehicle. At least a portion of a path to a target location corresponding to the received geographical location is calculated using the generated representation of the at least portion of the environment surrounding the vehicle. At least one command is provided to automatically navigate the vehicle based on the determined path and updated sensor data from at least a portion of the one or more sensors of the vehicle.

Autonomous and user controlled vehicle summon to a target

A processor coupled to memory is configured to receive image data based on an image captured by a camera of a vehicle. The image data is used as a basis of an input to a trained machine learning model trained to predict a three-dimensional trajectory of a machine learning feature. The three-dimensional trajectory of the machine learning feature is provided for automatically controlling the vehicle.

Predicting three-dimensional features for autonomous driving

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San Francisco, CA, United States of America

Andrej Karpathy