Pure Storage, Inc.

Ghost Autonomy Inc.

Ghost Locomotion Inc.

Applied Intuition, Inc.

John Martin Hayes

John Colgrove

Ethan L Miller

John D Davis

Robert H Lee

Zhangxi Tan

Volkmar Uhlig

Cary A Sandvig

Bo Hong

Feng Wang

Peter Vajgel

Brian Gold

Hari Kannan

Par Botes

Nenad Miladinovic

Shantanu R Gupta

Igor Ostrovsky

Craig Harmer

Jianting Cao

Christopher Golden

Joshua Robinson

Joseph S Hasbani

Akash J Sagar

Nima Soltani

Grigori Inozemtsev

Aaron Carroll

Rusty Sears

Feng Tian

Martin Harriman

Justin Erickson

Richard A Swetz

Daniel P Potts

Joshua P Robinson

Christopher R Lumb

Mario Delgado

Marco Sanvido

Richard A Hankins

Sergey Zhuravlev

Steve Hodgson

Andrew Kleinerman

Faissal Sleiman

Alex Ho

Shaun M Pacheco

James S Cross

Riaz Ali

Daniel D Schmidt

David Stephenson

Prannay Khosla

Robert Martin Lee

Daniel J Fillingham

Timothy Cerexhe

Noah Shaw

Basheer Tome

Matthew D Markel

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

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

Scheduling state transitions in an autonomous vehicle, including: detecting a transition signal for transitioning from a first state associated with a first machine learning model to a second state associated with a second machine learning model; determining whether a precondition for generating output by the second machine learning model has been satisfied; and delaying, in response for the precondition not being satisfied, a transition from the first state to the second state.

Scheduling state transitions in an autonomous vehicle

In some embodiments, a method for die-level monitoring is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are housed within a chassis that couples the storage nodes. Each of the storage nodes has a non-volatile solid-state storage with non-volatile memory and the user data is accessible via the erasure coding from a remainder of the storage nodes in event of two of the storage nodes being unreachable. The method includes producing diagnostic information that diagnoses the non-volatile memory on a basis of per package, per die, per plane, per block, or per page, the producing performed by each of the plurality of storage nodes. The method includes writing the diagnostic information to a memory in the storage cluster.

Error correction incident tracking

A computer system comprising: a data storage medium comprising a plurality of storage devices configured to store data; and a data storage controller coupled to the data storage medium; wherein the data storage controller is configured to: determine a target number of storage devices in a group of storage devices in a storage system that may exhibit variable I/O response times; detect one or more conditions associated with the group of storage devices; and after detecting the one or more conditions, change the target number of storage devices that may exhibit variable I/O response times.

Balancing the number of read operations and write operations that may be simultaneously serviced by a storage system

A method for preserving a media access control (MAC) address of a virtual server is provided. The method includes assigning a physical computing resource to a virtual server, assigning a physical storage memory resource to the virtual server, and assigning a physical network resource to the virtual server. The method includes assigning a virtual MAC address to the virtual server, the virtual MAC address to remain with the virtual server despite reassignment of one or more of the physical computing resource, the physical storage memory resource or the physical network resource, wherein at least one method operation is performed by a processor. A computing and storage system is also provided.

Preserved addressing for replaceable resources

A method for adjustable error correction in a storage cluster is provided. The method includes determining health of a non-volatile memory of a non-volatile solid-state storage unit of each of a plurality of storage nodes in a storage cluster on a basis of per flash package, per flash die, per flash plane, per flash block, or per flash page. The determining is performed by the storage cluster. The plurality of storage nodes is housed within a chassis that couples the storage nodes as the storage cluster. The method includes adjusting erasure coding across the plurality of storage nodes based on the health of the non-volatile memory and distributing user data throughout the plurality of storage nodes through the erasure coding. The user data is accessible via the erasure coding from a remainder of the plurality of storage nodes if any of the plurality of storage nodes are unreachable.

Wear levelling for differing memory types

A method for a transactional commit in a storage unit is provided. The method includes receiving a logical record from a storage node into a transaction engine of a storage unit of the storage node and writing the logical record into a data structure of the transaction engine. The method includes writing, to a command queue of the transaction engine, an indication to perform an atomic update using the logical record and transferring each portion of the logical record from the data structure of the transaction engine to non-persistent memory of the storage unit as a committed transaction. A storage unit for a storage system is also provided.

Maintaining coherency in a distributed system

A system and method for managing tables in a storage system is described.

Flash system having multiple fingerprint tables

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

Adjusting a variable parameter to increase reliability of stored data

A plurality of storage nodes within a single chassis is provided. The plurality of storage nodes is configured to communicate together as a storage cluster. The plurality of storage nodes has a non-volatile solid-state storage for user data storage. The plurality of storage nodes is configured to distribute the user data and metadata associated with the user data throughout the plurality of storage nodes, with erasure coding of the user data. The plurality of storage nodes is configured to recover from failure of two of the plurality of storage nodes by applying the erasure coding to the user data from a remainder of the plurality of storage nodes. The plurality of storage nodes is configured to detect an error and engage in an error recovery via one of a processor of one of the plurality of storage nodes, a processor of the non-volatile solid state storage, or the flash memory.

Data rebuild based on solid state memory characteristics

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

A storage system is provided. The storage system includes a plurality of non-volatile memory units and a processor operatively coupled to a plurality of non-volatile memory units. The processor is to perform a method including receiving a request to read data from the storage system. The method also includes determining whether a storage operation should be delayed, based on the request to read the data from the storage system. The method further includes in response to determining that the storage operation should be delayed, delaying the storage operation. The method further includes performing a read operation for the request to read the data.

Intelligent operation scheduling based on latency of operations

Reducing camera throughput to downstream systems using an intermediary device, including: receiving, by a device and from a camera via a first data link between the device and the camera, a frame; selecting, by the device, an area of focus for the frame; generating, by the device from the frame, a downsampled frame and a cropped frame, wherein the cropped frame is based on the area of focus; and providing, by the device to a computing system of an autonomous vehicle via a second data link between the device and the computing system, the downsampled frame and the cropped frame instead of the frame, wherein the second data link has a lower bandwidth than the first data link.

Reducing camera throughput to downstream systems using an intermediary device

Growing community of inventors

Mountain View, CA, United States of America

John Martin Hayes