Tesla, Inc.

Ashok Kumar Elluswamy

Andrej Karpathy

Christopher Payne

Matthew Bauch

Swupnil Kumar Sahai

James Anthony Musk

Joseph Polin

Arvind Ramanandan

Dhaval Shroff

James Robert Howard Hakewill

Nishant Desai

Philip Lee

Pengfei Duan

Disclosed herein are methods and systems for using artificial intelligence modeling techniques to train and execute an artificial intelligence model to analyze camera feed received from an ego to generate an occupancy data indicating whether different voxels within the ego's surroundings are occupied by an object having mass. A method comprises inputting, using a camera of an ego object, image data of a space around the ego object into an artificial intelligence model; predicting, by executing the artificial intelligence model, an occupancy attribute of a plurality of voxels; and generating a dataset based on the plurality of voxels and their corresponding occupancy attribute.

Artificial intelligence modeling techniques for vision-based occupancy determination

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