Aurora Operations, Inc.

Other

Aurora Innovation, Inc.

Nec Laboratories America, Inc.

Nec Research Institute, Inc.

Shriners Hospitals for Children

Glaspec Corporation

Warren Smith

Ethan Eade

Christopher Paul Urmson

James Andrew Bagnell

Sterling J Anderson

Bartholomeus C Nabbe

Sebastien Roy

Shaogang Wang

Richard A Linke

Anita Bagley

Donald Pank

Allan Lee Schweitzer

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

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

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.

Various implementations described herein generate training instances that each include corresponding training instance input that is based on corresponding sensor data of a corresponding autonomous vehicle, and that include corresponding training instance output that is based on corresponding sensor data of a corresponding additional vehicle, where the corresponding additional vehicle is captured at least in part by the corresponding sensor data of the corresponding autonomous vehicle. Various implementations train a machine learning model based on such training instances. Once trained, the machine learning model can enable processing, using the machine learning model, of sensor data from a given autonomous vehicle to predict one or more properties of a given additional vehicle that is captured at least in part by the sensor data.

Training machine learning model based on training instances with: training instance input based on autonomous vehicle sensor data, and training instance output based on additional vehicle sensor data

A hood prop member for a vehicle is provided having a support rod that has a first end configured to attach proximate a firewall of the vehicle, and a second end configured to couple proximate to a body panel of the vehicle. A prop rod having a connection end and a pivoting end is provided. The pivoting end is pivotingly attached proximate an end of the support rod. A hood attachment bracket is configured to attach to a hood of the vehicle. A storage connection point is attached to the support rod proximate an end opposite an end to which the pivoting end of the prop rod is proximately coupled. The connection end is attachable to the storage connection point in a hood-down position. The connection end is also attachable to the hood attachment bracket and configured to maintain the hood of the vehicle in a hood-raised position.

Vehicle hood prop member and related method

A firearm display device comprises a body and a mounting rod projecting from the body. The mounting rod comprises a first diameter configured to accept a firearm muzzle bore of a predetermined firearm caliber. A caliber identifier corresponds to the predetermined caliber. A first hole in the body comprises a second diameter, wherein the second diameter comprises a diameter that accepts the caliber identifier.

Firearm display and storage device and method

A method includes obtaining a first track associated with a first time. A first track associated with a first time is obtained. First predicted state data associated with a second time that is later than the first time, are generated based on the first track. Radar measurement data associated with the second time are obtained from one or more radar sensors. Track data are generated by a machine learning model based on the first predicted state data and the radar measurement data. Second predicted state data associated with the second time are generated based on the first track. A second track associated with the second time is generated based on the track data and the second predicted state data. The second track associated with the second time is provided to an autonomous vehicle control system for autonomous control of a vehicle.

Generating data using radar observation model based on machine learning

Sensors and methods for automatically detecting and recording physical activities of an individual are described. An event is detected, in which data representative of at least one physical activity of an individual is received from an activity monitor associated with the individual. The data is combined into at least two intermediate calculations. A score is generated from the at least two intermediate calculations. The score is compared to a predefined threshold to decide whether or not an event has occurred. Data representative of the event is recorded.

Activity monitoring

The present invention is a system and method for three dimensional machine vision including a projector and camera. The projector casts a structured light Quadratic Residue Bar Code on the surface to be mapped. The camera then images the surface. The projected image is shifted, and the process is repeated a number of times, according to the particular arrangement selected for the structured light Quadratic Residue Bar Code. The image data collected from this series of images is then processed by relatively simple matrix operations to determine the location of each camera pixel in three dimensional space.

Growing community of inventors

McKees Rocks, PA, United States of America

Warren Smith