Tesla, Inc.

Ashok Kumar Elluswamy

Andrej Karpathy

Christopher Payne

Matthew Bauch

James Anthony Musk

Swupnil Kumar Sahai

Joseph Polin

Arvind Ramanandan

Dhaval Shroff

James Robert Howard Hakewill

A system is comprised of one or more processors coupled to memory. The one or more processors are configured to receive image data based on an image captured using a camera of a vehicle and to utilize the image data as a basis of an input to a trained machine learning model to at least in part identify a distance of an object from the vehicle. The trained machine learning model has been trained using a training image and a correlated output of an emitting distance sensor.

Estimating object properties using visual image data

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

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Sunnyvale, CA, United States of America

Ashok Kumar Elluswamy