Tesla, Inc.

Christopher Payne

Ashok Kumar Elluswamy

Andrej Karpathy

Matthew Bauch

Joseph Polin

Arvind Ramanandan

Dhaval Shroff

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 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

Growing community of inventors

San Francisco, CA, United States of America

Christopher Payne