Waymo LLC

Google Inc.

Nuro Corp.

Facebook, Inc.

Other

Md Systems, Inc.

Vmware, Inc.

X Development LLC

Intogreat Companies, Inc.

Game Changer Holdings, LLC

David Ian Ferguson

Jiajun Zhu

Dmitri A Dolgov

Nathaniel Fairfield

David Harrison Silver

Pichayut Jirapinyo

Nan Ransohoff

Cosimo Leipold

Wan-Yen Lo

Abhijit S Ogale

Bradley Templeton

Jonathan Baldwin Dowdall

Christopher Paul Urmson

Donald Jason Burnette

Anthony Scott Levandowski

Dirk Haehnel

Bradley Ray Green

Russell Leigh Smith

Yangli Hector Yee

Jireh Yiwei Tan

Alan R Wolf

Jun Sun

Thomas Giovanni Carriero

Li Ju

Jeffrey Andrew Kanter

Blaise Andrew DiPersia

Ian James Mahon

Michael A Smyser

Russell Eric Heddleston

Andreas Wendel

Ioan-Alexandru Sucan

Dani Suleman

Brandon Douglas Luders

Stéphane Ross

Wenfeng Liu

Zhinan Xu

Pratik Agarwal

Benjamin Wade Julian

Yi Shen

Matthew Wang

Calvin Giroud

Xun Gong

Nick Vines

Kyle Philip Meyer

Pooja Jain

Dwain Crackel

Victor A Kean

Philip Pointer

Turgay Senlet

Qunyan Pu

Hyman Murveit

Matthew Jeremy Sarett

Michael Toback

Ronald L Wiley

Jerry Ju

Thomas VanFossen

Ben Julian

Ryan Keating

Maria Del Carmen Hernandez-Villavicencio

Monty Ijzerman

Patrick Williams

James Ratliff

Nicholas David Carlevaris-Bianco

Wilbur C Blomster

Anthony Ledandowski

According to one aspect, a method includes obtaining a request to remotely operate a robotic assistant, the robotic assistant including at least one sensor configured to collect data associated with an environment around the robotic assistant. The method also includes identifying a first teleoperations system to remotely operate the robotic assistant in response to the request, the first teleoperations system being one of a plurality of teleoperations systems, and assigning the first teleoperations system to remotely operate the robotic assistant. At least one privacy-related instruction is implemented with respect to the first teleoperations system.

Framework for robotic assistants

Aspects of the disclosure relate to determining whether a feature of map information. For example, data identifying an object detected in a vehicle's environment and including location coordinates is received. This information is used to identify a corresponding feature from pre-stored map information based on a map location of the corresponding feature. The corresponding feature is defined as a curve and associated with a tag identifying a type of the corresponding object. A tolerance constraint is identified based on the tag. The curve is divided into two or more line segments. Each line segment has a first position. The first position of a line segment is changed in order to determine a second position based on the location coordinates and the tolerance constraint. A value is determined based on a comparison of the first position to the second position. This value indicates a likelihood that the corresponding feature has changed.

Change detection using curve alignment

Aspects of the disclosure relate to an autonomous vehicle that may detect other nearby vehicles and designate stationary vehicles as being in one of a short-term stationary state or a long-term stationary state. This determination may be made based on various indicia, including visible indicia displayed by the detected vehicle and traffic control factors relating to the detected vehicle. For example, the autonomous vehicle may identify a detected vehicle as being in a long-term stationary state based on detection of hazard lights being displayed by the detected vehicle, as well as the absence of brake lights being displayed by the detected vehicle. The autonomous vehicle may then base its control strategy on the stationary state of the detected vehicle.

Determining the stationary state of detected vehicles

Methods and devices for using a relationship between activities of different traffic signals in a network to improve traffic signal state estimation are disclosed. An example method includes determining that a vehicle is approaching an upcoming traffic signal. The method may further include determining a state of one or more traffic signals other than the upcoming traffic signal. Additionally, the method may also include determining an estimate of a state of the upcoming traffic signal based on a relationship between the state of the one or more traffic signals other than the upcoming traffic signal and the state of the upcoming traffic signal.

Use of relationship between activities of different traffic signals in a network to improve traffic signal state estimation

Provided herein is an autonomous or semi-autonomous vehicle fleet comprising a plurality of electric autonomous vehicles for apportioned display of a media, operating autonomously and a fleet management module for coordination of the autonomous vehicle fleet. Each autonomous or semi-autonomous vehicle comprising a screen configured to display the media. Activation, deactivation, brightness modification, in combination with specific media selection enables more efficient media display.

Method and apparatus for displaying media on an autonomous vehicle

A method and apparatus are provided for determining one or more behavior models used by an autonomous vehicle to predict the behavior of detected objects. The autonomous vehicle may collect and record object behavior using one or more sensors. The autonomous vehicle may then communicate the recorded object behavior to a server operative to determine the behavior models. The server may determine the behavior models according to a given object classification, actions of interest performed by the object, and the object's perceived surroundings.

Automated system and method for modeling the behavior of vehicles and other agents

Methods and devices for actively modifying a field of view of an autonomous vehicle in view of constraints are disclosed. In one embodiment, an example method is disclosed that includes causing a sensor in an autonomous vehicle to sense information about an environment in a first field of view, where a portion of the environment is obscured in the first field of view. The example method further includes determining a desired field of view in which the portion of the environment is not obscured and, based on the desired field of view and a set of constraints for the vehicle, determining a second field of view in which the portion of the environment is less obscured than in the first field of view. The example method further includes modifying a position of the vehicle, thereby causing the sensor to sense information in the second field of view.

Actively modifying a field of view of an autonomous vehicle in view of constraints

Aspects of the invention relate generally to autonomous vehicles. The features described improve the safety, use, driver experience, and performance of these vehicles by performing a behavior analysis on mobile objects in the vicinity of an autonomous vehicle. Specifically, the autonomous vehicle is capable of detecting nearby objects, such as vehicles and pedestrians, and is able to determine how the detected vehicles and pedestrians perceive their surroundings. The autonomous vehicle may then use this information to safely maneuver around all nearby objects.

System and method for predicting behaviors of detected objects through environment representation

Aspects of the present disclosure relate to differentiating between active and inactive construction zones. In one example, this may include identifying a construction object associated with a construction zone. The identified construction object may be used to map the area of the construction zone. Detailed map information may then be used to classify the activity of the construction zone. The area of the construction zone and the classification may be added to the detailed map information. Subsequent to adding the construction zone and the classification to the detailed map information, the construction object (or another construction object) may be identified. The location of the construction object may be used to identify the construction zone and classification from the detailed map information. The classification of the classification may be used to operate a vehicle having an autonomous mode.

Mapping active and inactive construction zones for autonomous driving

Disclosed herein are systems and methods for providing supplemental identification abilities to an autonomous vehicle system. The sensor unit of the vehicle may be configured to receive data indicating an environment of the vehicle, while the control system may be configured to operate the vehicle. The vehicle may also include a processing unit configured to analyze the data indicating the environment to determine at least one object having a detection confidence below a threshold. Based on the at least one object having a detection confidence below a threshold, the processor may communicate at least a subset of the data indicating the environment for further processing. The vehicle is also configured to receive an indication of an object confirmation of the subset of the data. Based on the object confirmation of the subset of the data, the processor may alter the control of the vehicle by the control system.

Assisted perception for autonomous vehicles

Methods and devices for detecting traffic signals and their associated states are disclosed. In one embodiment, an example method includes a scanning a target area using one or more sensors of a vehicle to obtain target area information. The vehicle may be configured to operate in an autonomous mode, and the target area may be a type of area where traffic signals are typically located. The method may also include detecting a traffic signal in the target area information, determining a location of the traffic signal, and determining a state of the traffic signal. Also, a confidence in the traffic signal may be determined. For example, the location of the traffic signal may be compared to known locations of traffic signals. Based on the state of the traffic signal and the confidence in the traffic signal, the vehicle may be controlled in the autonomous mode.

Robust method for detecting traffic signals and their associated states

A delivery system includes a database configured to store information of a customer where the information includes a default payment method, a communication system configured to communicate with an autonomous vehicle and with a device of the customer, where the device includes a display screen having a button to summon an autonomous vehicle, at least one processor, and a memory storing instructions. The instructions, when executed by the at least one processor, cause the delivery system to receive an indication via the communication system that the button on the device of the customer has been clicked, and instruct the autonomous vehicle to travel to a location of the customer.

Systems and methods for one-click delivery of autonomous vehicle

A vehicle configured to operate in an autonomous mode may operate a sensor to determine an environment of the vehicle. The sensor may be configured to obtain sensor data of a sensed portion of the environment. The sensed portion may be defined by a sensor parameter. Based on the environment of the vehicle, the vehicle may select at least one parameter value for the at least one sensor parameter such that the sensed portion of the environment corresponds to a region of interest. The vehicle may operate the sensor, using the selected at least one parameter value for the at least one sensor parameter, to obtain sensor data of the region of interest, and control the vehicle in the autonomous mode based on the sensor data of the region of interest.

Modifying the behavior of an autonomous vehicle using context based parameter switching

Methods and systems for the use of detected objects for image processing are described. A computing device autonomously controlling a vehicle may receive images of the environment surrounding the vehicle from an image-capture device coupled to the vehicle. In order to process the images, the computing device may receive information indicating characteristics of objects in the images from one or more sources coupled to the vehicle. Examples of sources may include RADAR, LIDAR, a map, sensors, a global positioning system (GPS), or other cameras. The computing device may use the information indicating characteristics of the objects to process received images, including determining the approximate locations of objects within the images. Further, while processing the image, the computing device may use information from sources to determine portions of the image to focus upon that may allow the computing device to determine a control strategy based on portions of the image.

Use of detected objects for image processing

Methods and systems are disclosed for determining sensor degradation by actively controlling an autonomous vehicle. Determining sensor degradation may include obtaining sensor readings from a sensor of an autonomous vehicle, and determining baseline state information from the obtained sensor readings. A movement characteristic of the autonomous vehicle, such as speed or position, may then be changed. The sensor may then obtain additional sensor readings, and second state information may be determined from these additional sensor readings. Expected state information may be determined from the baseline state information and the change in the movement characteristic of the autonomous vehicle. A comparison of the expected state information and the second state information may then be performed. Based on this comparison, a determination may be made as to whether the sensor has degraded.

Detecting sensor degradation by actively controlling an autonomous vehicle

Growing community of inventors

San Francisco, CA, United States of America

David Ian Ferguson