Ghost Autonomy Inc.

Applied Intuition, Inc.

Ghost Locomotion Inc.

Nima Soltani

John Martin Hayes

Volkmar Uhlig

Feng Tian

Akash J Sagar

Christopher R Lumb

Processing predictive input data in an autonomous vehicle, including: receiving input data for a model; determining whether the input data for the model comprises an indication that the input data was generated based on some amount of predicted data; generating, by the model and based on the input data, output data by modifying, in response to the input data comprising the indication, one or more thresholds or one or more confidence scores of the model used in generating output data; and causing an autonomous vehicle to perform one or more driving decisions based on the output data of the model.

Processing predicted input data in an autonomous vehicle

Modifying autonomous vehicle sensors cameras based on detected lighting boundaries, including: determining, based on video data, that a vehicle will pass through a lighting boundary; determining, based on the video data, that an illumination of a predicted input to a camera of the vehicle after passing the lighting boundary falls outside a range of illumination based on a current exposure setting of the camera; and modifying, in response to determining that the illumination of the predicted input falls outside the range of illumination, an exposure setting of one or more cameras.

Modifying autonomous vehicle cameras based on detected lighting boundaries

Selective training of neural networks using motion estimation, including: selecting, from a plurality of pixels in video data from a vehicle, based on motion relative to the vehicle, one or more pixels; and training a neural network based on the video data by zeroing out an error function applied to the selected one or more pixels.

Selective training of neural networks using motion estimation

Modifying settings of autonomous vehicle sensors based on predicted environmental states, including: determining, based on sensor data, that a predicted environmental state of the autonomous vehicle is associated with a sensory input outside an operating range of a sensor; determining a modified operating range of the sensor; and modifying one or more sensors to operate according to the modified operating range.

Modifying settings of autonomous vehicle sensors based on predicted environmental states

Determining control operations for an autonomous vehicle may include receiving, by an operational model, an operational model input based on camera data from one or more cameras of an automated vehicle; determining, by the operational model, based on the input, a current environmental state and a predicted environmental state; providing, to a rules module, a differential between the current environmental state and a previously predicted environmental state; and determining, based on the rules module, one or more control operations for the automated vehicle.

Determining control operations for an autonomous vehicle

Camera data normalization for an autonomous vehicle are described herein, including: receiving, from one or more cameras of the autonomous vehicle, camera data; applying a color normalization to the camera data; applying a spherical reprojection to the camera data; and applying, based on a registration point, a stabilization to the camera data.

Camera data normalization for an autonomous vehicle

Handling input data errors in an autonomous vehicle using predictive inputs, including: determining an error in input data for a model of a plurality of models of an automation system of the autonomous vehicle; generating predicted input data for the model; and generating, based on the predicted input data, output data for the model.

Handling input data errors in an autonomous vehicle

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Detecting out-of-model scenarios for an autonomous vehicle

Scene filtering using motion estimation, including identifying, in camera data from an autonomous vehicle, based on motion relative to the autonomous vehicle, one or more pixels; filtering, from the camera data, the one or more pixels; and training, based on the filtered camera data, a neural network.

Scene filtering using motion estimation

Controlling an automated vehicle using visual anchors, including receiving, from one or more cameras of an autonomous vehicle, first video data; identifying one or more visual anchors in the first video data; determining one or more differentials between the one or more visual anchors and one or more predicted visual anchors; and determining, based on the one or more differentials, one or more control operations for the autonomous vehicle to reduce the one or more differentials.

Growing community of inventors

Los Gatos, CA, United States of America

Nima Soltani