Aurora Operations, Inc.

The Toro Company

Aurora Innovation, Inc.

Adam Richard Williams

Abhay Vardhan

Yekeun Jeong

Ethan Eade

James Andrew Bagnell

David LaRose

Alexander Steven Frick

Ryan Douglas Ingvalson

Jason Thomas Kraft

Michael Jason Ramsay

Christopher Charles Osterwood

Zachary Irvin Parker

Stephen Paul Elizondo Landers

Nicholas George Dilip Roy

David Arthur Larose

Brian Daniel Beyer

James Scott Jackson

Systems and methods for localization of a trailer of an autonomous tractor-trailer are described herein. Some implementations can determine a sector area in an environment of the autonomous tractor-trailer that is predicted to include the trailer, determine a subset of an LIDAR data that is generated by LIDAR sensor(s) of an autonomous tractor of the autonomous tractor-trailer and that is predicted to include the trailer based on the sector area, generate a trailer pose instance of a trailer pose of the trailer based on the subset of the LIDAR data, and cause the trailer pose instance to be utilized in controlling the autonomous tractor-trailer. Additional or alternative implementations can utilize particular LIDAR sensor(s) in generating the trailer pose instance, such as phase coherent LIDAR sensor(s) or polarized LIDAR sensor(s).

Localization methods and architectures for a trailer of an autonomous tractor-trailer

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

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

Autonomous machine navigation techniques may generate a three-dimensional point cloud that represents at least a work region based on feature data and matching data. Pose data associated with points of the three-dimensional point cloud may be generated that represents poses of an autonomous machine. A boundary may be determined using the pose data for subsequent navigation of the autonomous machine in the work region. Non-vision-based sensor data may be used to determine a pose. The pose may be updated based on the vision-based pose data. The autonomous machine may be navigated within the boundary of the work region based on the updated pose. The three-dimensional point cloud may be generated based on data captured during a touring phase. Boundaries may be generated based on data captured during a mapping phase.

Autonomous machine navigation and training using vision system

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San Francisco, CA, United States of America

Adam Richard Williams