Cloudera, Inc.

Ghost Autonomy Inc.

Applied Intuition, Inc.

Justin Erickson

Henry Noel Robinson

Nong Li

Alex Behm

Marcel Kornacker

Lenni Kuff

Alan Choi

John Martin Hayes

Volkmar Uhlig

Aaron Carroll

Mario Delgado

Akash J Sagar

Shaun M Pacheco

James S Cross

Riaz Ali

Daniel D Schmidt

Noah Shaw

Basheer Tome

Li Nong

Reducing throughput of paired cameras, including: selecting, by a first camera of an autonomous vehicle, a first area of focus for a first frame generated by the first camera; selecting, by a second camera of the autonomous vehicle, a second area of focus for a second frame generated by the second camera by matching the first area of focus for the first frame; generating, by the first camera from the first frame, a first downsampled frame and a first cropped frame, wherein the first cropped frame is based on the first area of focus; and generating, by the second camera from the second frame, a second downsampled frame and a second cropped frame, wherein the second cropped frame is based on the second area of focus.

Reducing throughput of paired cameras

Disengagement of an autonomous driving mode based on vehicle operating conditions, including: receiving, from a steering torque sensor, torque sensor data indicating an amount of torque applied to a steering system of an autonomous vehicle; determining a predicted torque based on one or more motion attributes of the steering system of the autonomous vehicle; determining an estimated operator-provided torque as a differential between the predicted torque and the amount of torque; selecting a threshold from a plurality of thresholds, wherein each threshold of the plurality of thresholds corresponds to a different possible operating condition of the autonomous vehicle; and disengaging an autonomous driving mode of the autonomous vehicle based on the differential exceeding the threshold.

Disengagement of an autonomous driving mode based on vehicle operating conditions

Blended operator and autonomous control in an autonomous vehicle, including: receiving sensor data from a plurality of sensors of an autonomous vehicle; determining, based on the sensor data, a degree of autonomous control for each control input of a plurality of control inputs; and applying the degree of autonomous control for each control input of the plurality of control inputs.

Blended operator and autonomous control in an autonomous vehicle

Throughput reduction in autonomous vehicle camera sensors, including: generating, by a camera sensor, a frame; selecting an area of focus for the frame; and generating, by the camera sensor from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus.

Throughput reduction in autonomous vehicle camera sensors

A format conversion engine for Apache Hadoop that converts data from its original format to a database-like format at certain time points for use by a low latency (LL) query engine. The format conversion engine comprises a daemon that is installed on each data node in a Hadoop cluster. The daemon comprises a scheduler and a converter. The scheduler determines when to perform the format conversion and notifies the converter when the time comes. The converter converts data on the data node from its original format to a database-like format for use by the low latency (LL) query engine.

Background format optimization for enhanced queries in a distributed computing cluster

Automatic disengagement of an autonomous driving mode may include receiving, from a steering torque sensor, torque sensor data indicating an amount of torque applied to a steering system of the autonomous vehicle; determining a predicted torque based on one or more motion attributes of the steering system of the autonomous vehicle; determining a differential between the predicted torque and the amount of torque; and determining, based on the differential, whether to disengage an autonomous driving mode of the autonomous vehicle.

Automatic disengagement of an autonomous driving mode

Background format optimization for enhanced SQL-like queries in Hadoop

A low latency query engine for APACHE HADOOP&#x2122; that provides real-time or near real-time, ad hoc query capability, while completing batch-processing of MapReduce. In one embodiment, the low latency query engine comprises a daemon that is installed on data nodes in a HADOOP&#x2122; cluster for handling query requests and all internal requests related to query execution. In a further embodiment, the low latency query engine comprises a daemon for providing name service and metadata distribution. The low latency query engine receives a query request via client, turns the request into collections of plan fragments and coordinates parallel and optimized execution of the plan fragments on remote daemons to generate results at a much faster speed than existing batch-oriented processing frameworks.

Low latency query engine for apache hadoop

Low latency query engine for Apache Hadoop

Growing community of inventors

San Francisco, CA, United States of America

Justin Erickson