Waymo LLC

Kodiak Robotics, Inc.

Google Inc.

Andreas Wendel

David Ian Ferguson

Jeremy Dittmer

Benjamin T Ingram

Court Hinricher

Jay Kuvelker

John Zinn

Donald Burnette

Laura W Shane

David Ian Franklin Ferguson

Brendan Hermalyn

Nathaniel Fairfield

Dmitri A Dolgov

Jens-Steffen Ralf Gutmann

Donald Jason Burnette

Blaise Gassend

Christopher Kennedy Ludwick

Felix Jose Alvarez Rivera

Mark Alexander Shand

Kimberly Geneva Toth

Shane Mcguire

Derek J Phillips

Collin C Otis

Jackson P Rusch

Zachary Morriss

Laurens Andreas Feenstra

Volker Grabe

Carl Warren Craddock

Abhijit S Ogale

Michael R James

Chen Wu

Daniel L Rosenband

Christian Lauterbach

Edward Hsiao

Etai Bruhis

Pieter Kapsenberg

Alexander Zbrozek

Ryan Joseph Andrade

Emily Chi

Zhuyuan Liu

Maxim Krivokon

David Harrison Silver

Nicholas Armstrong-Crews

Brandon Douglas Luders

Zhinan Xu

Clayton Kunz

Felix Duvallet

A sensor assembly for autonomous vehicles includes a side mirror assembly configured to mount to a vehicle. The side mirror assembly includes a first camera having a field of view in a direction opposite a direction of forward travel of the vehicle; a second camera having a field of view in the direction of forward travel of the vehicle; and a third camera having a field of view in a direction substantially perpendicular to the direction of forward travel of the vehicle. The first camera, the second camera, and the third camera are oriented to provide, in combination with a fourth camera configured to be mounted on a roof of the vehicle, an uninterrupted camera field of view from the direction of forward travel of the vehicle to a direction opposite the direction of forward travel of the vehicle.

Sensor assembly with lidar and radar for autonomous vehicles

The present disclosure is directed to an autonomous vehicle having a vehicle control system. The vehicle control system includes an image processing system. The image processing system receives an image that includes a plurality of image portions. The image processing system also calculates a score for each image portion. The score indicates a level of confidence that a given image portion represents an illuminated component of a traffic light. The image processing system further identifies one or more candidate portions from among the plurality of image portions. Additionally, the image processing system determines that a particular candidate portion represents an illuminated component of a traffic light using a classifier. Further, the image processing system provides instructions to control the autonomous vehicle based on the particular candidate portion representing an illuminated component of a traffic light.

Vision-based detection and classification of traffic lights

Systems and methods for controlling a vehicle are provided. The system may comprise a vehicle, one or more sensors, one or more actuation controls configured to enable the vehicle to perform one or more driving actions, and a controller, comprising a processor, configured to receive one or more trajectory commands, automatically generate an automatic trajectory command, and generate one or more driving actions. The one or more driving actions may correlate to one or more actuator commands configured to cause the vehicle to be positioned in accordance with the one or more trajectory plot points of the automatic trajectory command. The system may comprise a remote station system comprising one or more remote actuation controls configured generate the one or more driving actions of the remote trajectory command. The remote station system may be configured to generate one or more driving actions.

Systems and methods of controlling a vehicle incorporating remote, in-vehicle, and autonomously generated data

Sensor assembly with radar for autonomous vehicles

This disclosure presents systems and methods for path planning in autonomous vehicles. The method involves a planner receiving perception data from a perception module, which includes tracking or predicted object data about obstacles in the environment of an autonomous vehicle. The tracking or predicted object data are determined based on high recall detection data and high precision detection data.

Systems and methods for path planning of autonomous vehicles

Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.

Traffic signal response for autonomous vehicles

A sensor assembly for autonomous vehicles includes a side minor assembly configured to mount to a vehicle. The side mirror assembly includes a first camera having a field of view in a direction opposite a direction of forward travel of the vehicle; a second camera having a field of view in the direction of forward travel of the vehicle; and a third camera having a field of view in a direction substantially perpendicular to the direction of forward travel of the vehicle. The first camera, the second camera, and the third camera are oriented to provide, in combination with a fourth camera configured to be mounted on a roof of the vehicle, an uninterrupted camera field of view from the direction of forward travel of the vehicle to a direction opposite the direction of forward travel of the vehicle.

Sensor assembly for autonomous vehicles

Sensor assembly with lidar for autonomous vehicles

This disclosure provides systems and methods for controlling a vehicle based on a combination of high precision detection and high recall detection. The disclosed systems and methods can efficiently generate trajectories by reducing duplicate detection or duplicate calculation of objects or obstacles of common and known object types and objects or obstacles without class identification.

Systems and methods for controlling a vehicle using high precision and high recall detection

Systems and methods are provided of downsampling an image to a plurality of image resolutions. The method comprises capturing, using a camera, an image depicting an environment within view of the camera, identifying a first section of the image depicting an area of the environment spaced within a first distance range from the camera, identifying a second section of the image depicting an area of the environment spaced within a second distance range from the camera, identifying a third section of the image depicting an area of the environment spaced within a third distance range from the camera, and downsampling the first section of the image to a first image resolution, generating a first processed image, the second section of the image to a second image resolution, generating a second processed image, and the third section of the image to a third image resolution, generating a third processed image.

Systems and methods for downsampling images

The technology relates to autonomous vehicles that use a perception system to detect objects and features in the vehicle's surroundings. A camera assembly having a ring-type structure is provided that gives the perception system an overall 360° field of view around the vehicle. Image sensors are arranged in camera modules around the assembly to provide a seamless panoramic field of view. One subsystem has multiple pairs of image sensors positioned to provide the overall 360° field of view, while another subsystem provides a set of image sensors generally facing toward the front of the vehicle to provide enhanced object identification. The camera assembly may be arranged in a housing located on top of the vehicle. The housing may include other sensors such as LIDAR and radar. The assembly includes a chassis and top and base plates, which may provide EMI protection from other sensors disposed in the housing.

Camera ring structure for autonomous vehicles

Growing community of inventors

Mountain View, CA, United States of America

Andreas Wendel