Aurora Operations, Inc.

Irobot Corporation

Microsoft Technology Licensing, LLC

Aurora Innovation, Inc.

Uatc, LLC

Ethan Eade

Mario E Munich

Philip Fong

James Andrew Bagnell

Warren Smith

Christopher Paul Urmson

Michael Bode

Sterling J Anderson

Bartholomeus C Nabbe

Peter Hansen

Robert Zlot

David Prasser

Drew Edward Steedly

Michael Ebstyne

Dhiraj Goel

Jeroen Vanturennout

David Fields

Gavin Dean Lazarow

Tushar Cyril Bhatnagar

Frederik Schaffalitzky

Brett Browning

Nathaniel Gist, Iv

Jonathan Lyons

Thomas Pilarski

Jean-Sebastien Valois

Michael Grabner

Xxx Xinjilefu

Steffen Gutmann

Alex A Kipman

Bryan John Nagy

David LaRose

Brett Bavar

Adam Cole Panzica

Abhay Vardhan

Georg Klein

Adam Henry Polk Milstein

Daniel Munoz

Karthik Lakshmanan

Yekeun Jeong

Adam Richard Williams

Shaogang Wang

Luona Yang

Jake Charland

Samuel Sean

Gordon Peter Bailey

Stephen G Latta

Arthur Charles Tomlin

Martin Thomas Shetter

Tony Ambrus

Narek Melik-Barkhudarov

Matthew Sammis Ashman

Paul Albert Lalonde

Adam Milstein

Calvin Chan

Jonathan David Hildebrandt

Rachel Ahn

Alex Christopher Turner

Nicholas George Dilip Roy

Yuchen Xie

Frederick Schaffalitzky

Neena Kamath

James Scott Jackson

David Arthur Larose

Michael John Ebstyne

A computing device and method are provided for transmitting a relevant subset of map data, called a neighborhood, to enable mutual spatial understanding by multiple display devices around a target virtual location to display a shared hologram in the same exact location in the physical environment at the same moment in time. The computing device may comprise a processor, a memory operatively coupled to the processor, and an anchor transfer program stored in the memory and executed by the processor.

Sharing neighboring map data across devices

Determining an instantaneous vehicle characteristic (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined instantaneous vehicle characteristic of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined instantaneous vehicle characteristic of the additional vehicle. In many implementations, the instantaneous vehicle characteristics of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Control of autonomous vehicle based on determined yaw parameter(s) of additional vehicle

A method may include obtaining lidar data comprising a plurality of lidar returns from an environment of an autonomous vehicle. The lidar data may be processed with a machine learning model to generate, for the plurality of lidar returns, a plurality of first outputs that each identify a respective lidar return as belonging to an object or non-object and a plurality of second outputs that identify lidar returns belonging to objects as harmful or non-harmful to the autonomous vehicle. A subset of the lidar returns identified as belonging to objects that (i) do not correspond to any of a plurality of pre-classified objects and (ii) were identified as harmful to the autonomous vehicle may be determined. The autonomous vehicle may be controlled based at least in part on the subset of lidar returns.

System and method for generating information on remainder of measurement using sensor data

Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Control of autonomous vehicle based on environmental object classification determined using phase coherent LIDAR data

Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.

Carpet drift estimation using differential sensors or visual measurements

An autonomous vehicle uses a secondary vehicle control system to supplement a primary vehicle control system to perform a controlled stop if an adverse event is detected in the primary vehicle control system. The secondary vehicle control system may use a redundant lateral velocity determined by a different sensor from that used by the primary vehicle control system to determine lateral velocity for use in controlling the autonomous vehicle to perform the controlled stop.

Redundant lateral velocity determination and use in secondary vehicle control systems

Systems and methods for landmark-based localization of an autonomous vehicle ('AV') are described herein. Implementations can generate a first predicted location of a landmark based on a pose instance of a pose of the AV and a stored location of the landmark, generate a second predicted location of the landmark relative to the AV based on an instance of LIDAR data, generate a correction instance based on the comparing, and use the correction instance in generating additional pose instance(s). Systems and methods for validating localization of a vehicle are also described herein. Implementations can obtain driving data from a past episode of locomotion of the vehicle, generate a pose-based predicted location of a landmark in an environment of the vehicle, and compare the pose-based predicted location to a stored location of the landmark in the environment of the vehicle to validate a pose instance of a pose of the vehicle.

Landmark-based localization methods and architectures for an autonomous vehicle

One or more instances of sensor data collected using an autonomous vehicle sensor suite can be labeled using corresponding instance(s) of sensor data collected using an additional sensor suite. The additional vehicle can include a second autonomous vehicle as well as a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, an object in the sensor data captured using the autonomous vehicle can be labeled by mapping a label corresponding to the same object captured using the additional vehicle. In various implementations, labeled instances of sensor data can be utilized to train a machine learning model to generate one or more control signals for autonomous vehicle control.

Labeling autonomous vehicle data

Systems and methods for using transmission sensor(s) in localization of an autonomous vehicle ('AV') are described herein. Some implementations receive instance(s) of transmission sensor data generated by transmission sensor(s) of the AV, generate pose instance(s) of a pose of the AV based on the instance(s) of the transmission sensor data, and cause the AV to be controlled based on the pose instance(s). In some of those implementations, the pose instance(s) can be generated based on steering data the AV that indicates steering angle(s) of the autonomous, and that temporally correspond to the instance(s) of the transmission sensor data. In various implementations, the pose instance(s) may only be generated or utilized based on the instance(s) of the transmission sensor data (and optionally the steering data) in response to detecting an adverse event at the AV.

Using transmission sensor(s) in localization of an autonomous vehicle

Determining yaw parameter(s) (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined yaw parameter(s) of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined yaw rate of the additional vehicle. In many implementations, the yaw parameter(s) of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

Sensor data collected from an autonomous vehicle data can be labeled using sensor data collected from an additional vehicle. The additional vehicle can include a non-autonomous vehicle mounted with a removable hardware pod. In many implementations, removable hardware pods can be vehicle agnostic. In many implementations, generated labels can be utilized to train a machine learning model which can generate one or more control signals for the autonomous vehicle.

A method may include obtaining sensor data that include a plurality of sensor returns from an environment of an autonomous vehicle. A first set of features may be extracted from the sensor data. The first set of features may be processed with a machine learning model to generate, for at least a subset of the plurality of sensor returns, a first output that classifies a respective sensor return as corresponding to an object or non-object and a second output that indicates a property of the object. The sensor returns classified as corresponding to objects may be compared to a plurality of pre-classified objects to generate one or more generic object classifications. The autonomous vehicle may be controlled based at least in part on the one or more generic object classifications.

A method for receiving autonomous vehicle (AV) map data associated with an AV map of a geographic location and coverage map data associated with a coverage map of the geographic location. The AV map data is associated with an AV lane of a roadway in the geographic location, and the coverage map data is associated with a coverage lane of the roadway in the geographic location. The method includes generating a hybrid map of the geographic location based on the AV map data and the coverage map data and providing hybrid map data associated with the hybrid map for routing of an AV. The hybrid map includes the AV lane linked with the coverage lane of the roadway.

Map creation from hybrid data

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Pittsburgh, PA, United States of America

Ethan Eade