Google Inc.

Waymo LLC

Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Technical Center, U.s.a., Inc.

Dmitri A Dolgov

Christopher Paul Urmson

Jiajun Zhu

David Ian Ferguson

Philip Nemec

Nathaniel Fairfield

Manuel Christian Clement

Brian Douglas Cullinane

Robertus Christianus Elisabeth Mariet

Andrew Timothy Szybalski

Andrew Hughes Chatham

Dave Ferguson

Anne Kristina Aula

David Ian Franklin Ferguson

Sebastian Thrun

Andreas Wendel

Jens-Steffen Ralf Gutmann

Donald Jason Burnette

Abhijit S Ogale

Michael Steven Montemerlo

Vadim Furman

Jonathan Baldwin Dowdall

Joshua Seth Herbach

Luis Ricardo Prada Gomez

Jared Stephen Russell

Anthony Scott Levandowski

Peter Colijn

Laura Estelle Lindzey

Nirmal Jitendra Patel

Salil Pandit

Ryan Powell

Min Li Chan

Kenneth P Laberteaux

Ian James Mahon

Hyman Murveit

Michael R James

Daniel Trawick Egnor

Pratik Agarwal

Andrew Schultz

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

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

Cross-validating sensors of an autonomous vehicle

Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

Systems and methods for vehicles with limited destination ability

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

Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating in a rewind mode. In the rewind mode, the system may be configured to provide information to a remote assistance operated based on a remote-assistance triggering criteria being met, such as determining that the autonomous vehicle has been stopped for a period of time. When the triggering criteria is met, the remote assistance system may provide data from the time leading up to when the remote-assistance triggering criteria was met that was capture of the environment of autonomous vehicle to the remote assistance operator. Based on viewing the data, the remote assistance operator may provide and input to the system that causes a command to be issued to the autonomous vehicle.

Methods and systems for providing remote assistance to a stopped vehicle

A system and method are arranged to provide recommendations to a user of a vehicle. In one aspect, the vehicle navigates in an autonomous mode and the sensors provide information that is based on the location of the vehicle and output from sensors directed to the environment surrounding the vehicle. In further aspects, both current and previous sensor data is used to make the recommendations, as well as data based on the sensors of other vehicles.

System and method of providing recommendations to users of vehicles

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

Aspects of the disclosure relate generally to detecting and avoiding blind spots of other vehicles when maneuvering an autonomous vehicle. Blind spots may include both areas adjacent to another vehicle in which the driver of that vehicle would be unable to identify another object as well as areas that a second driver in a second vehicle may be uncomfortable driving. In one example, a computer of the autonomous vehicle may identify objects that may be relevant for blind spot detecting and may determine the blind spots for these other vehicles. The computer may predict the future locations of the autonomous vehicle and the identified vehicles to determine whether the autonomous vehicle would drive in any of the determined blind spots. If so, the autonomous driving system may adjust its speed to avoid or limit the autonomous vehicle's time in any of the blind spots.

Avoiding blind spots of other vehicles

A vehicle may receive one or more images of an environment of the vehicle. The vehicle may also receive a map of the environment. The vehicle may also match at least one feature in the one or more images with corresponding one or more features in the map. The vehicle may also identify a given area in the one or more images that corresponds to a a portion of the map that is within a threshold distance to the one or more features. The vehicle may also compress the one or more images to include a lower amount of details in areas of the one or more images other than the given area. The vehicle may also provide the compressed images to a remote system, and responsively receive operation instructions from the remote system.

Image and video compression for remote vehicle assistance

Aspects of the disclosure relate generally to speed control in an autonomous vehicle. For example, an autonomous vehicle may include a user interface which allows the driver to input speed preferences. These preferences may include the maximum speed above the speed limit the user would like the autonomous vehicle to drive when other vehicles are present and driving above or below certain speeds. The other vehicles may be in adjacent or the same lane the vehicle, and need not be in front of the vehicle.

Modifying speed of an autonomous vehicle based on traffic conditions

A light detection and ranging device associated with an autonomous vehicle scans through a scanning zone while emitting light pulses and receives reflected signals corresponding to the light pulses. The reflected signals indicate a three-dimensional point map of the distribution of reflective points in the scanning zone. A hyperspectral sensor images a region of the scanning zone corresponding to a reflective feature indicated by the three-dimensional point map. The output from the hyperspectral sensor includes spectral information characterizing a spectral distribution of radiation received from the reflective feature. The spectral characteristics of the reflective feature allow for distinguishing solid objects from non-solid reflective features, and a map of solid objects is provided to inform real time navigation decisions.

Characterizing optically reflective features via hyper-spectral sensor

Growing community of inventors

Los Altos, CA, United States of America

Dmitri A Dolgov