Waymo LLC

Salil Pandit

Nirmal Jitendra Patel

Michael Epstein

Renaud-Roland Hubert

John Wesley Dyer

Julien Charles Mercay

Clement Wright

Min Li Chan

Laurens Andreas Feenstra

Philip Nemec

Joshua Seth Herbach

Juliet Rothenberg

Saksiri Tanphaichitr

Matthew Corey Hall

Ryan Powell

Tacettin Dogacan Guney

Kevin Rawlings

Guilherme Villar

Mark Alexander Shand

Jonathan Lee Pedersen

Cristi Landy

Lorena Zalles

Reed Gerard Alexander Morse

Leonid Yeykelis

Jessica Gong

Lauren Tindal

Noah Eisen

Son-Ca Nguyen

Nathaniel Fairfield

Dmitri A Dolgov

Christopher Paul Urmson

Peter Crandall

Maria Moon

Peter Colijn

Mishika Vora

Bruce Mai

Lauren Schwendimann

Ben Qingzhou Wang

Konstantin Pervyshev

Robert J Chen

Ganesh Balachandran

Eric Friedman

Daniella Gutlansky

Gaurav Agarwal

Eamonn Michael Doherty

Andrew Chan

Julien Mercay

Guiherme Villar

The disclosure relates to using ambient lighting conditions with passenger and goods pickups and drop offs with autonomous vehicles. For instance, a map of ambient lighting conditions for stopping locations may be generated by receiving ambient lighting condition data for predetermined stopping locations and arranging this data into a plurality of buckets based on time and one of the stopping locations. A vehicle may then be controlled in an autonomous driving mode in order to stop for a passenger by both observing ambient lighting conditions for different stopping locations and, in some instances, also using the map.

Ambient lighting conditions for autonomous vehicles

The technology includes communicating the current status of a self-driving vehicle to users, such as passengers within the vehicle and other users awaiting pickup. Certain information about the trip and vehicle status is communicated depending on where the passenger is sitting within the vehicle or where the person awaiting pickup is located outside the vehicle. This includes disseminating the 'monologue' of a vehicle operating in an autonomous driving mode to a user via an app on the user's device (e.g., mobile phone, tablet or laptop PC, wearable, or other computing device) and/or an in-vehicle user interface. The monologue includes current status information regarding driving decisions and related operations or actions. This alerts the user as to why the vehicle is taking (or not taking) a certain action, which reduces confusion and allows the user to focus on other matters.

Application monologue for self-driving vehicles

The technology relates to actively looking for an assigned passenger prior to a vehiclereaching a pickup location. For instance, information identifying the pickup location and client device information for authenticating the assigned passenger is received. Sensor data is received from a perception system of the vehicle identifying objects in an environment of the vehicle. When the vehicle is within a predetermined distance from the pickup location, authenticating a client device using the client device information is attempted. When the client device has been authenticated, the sensor data is used to determine whether a pedestrian is within a first threshold distance of the vehicle. When a pedestrian is determined to be within the first threshold distance of the vehicle, the vehicle is stopped prior to reaching the pickup location, to wait for the pedestrian within the first threshold distance of the vehicle to enter the vehicle.

Early boarding of passengers in autonomous vehicles

Aspects of the disclosure provide a method of facilitating communications from an autonomous vehicle to a user. For instance, a method may include, while attempting to pick up the user and prior to the user entering an vehicle, inputting a current location of the vehicle and map information into a model in order to identify a type of communication action for communicating a location of the vehicle to the user; enabling a first communication based on the type of the communication action; determining whether the user has responded to the first communication from received sensor data; and enabling a second communication based on the determination of whether the user has responded to the communication.

Communications for autonomous vehicles

Aspects of the disclosure relate to generating map data. For instance, data generated by a perception system of a vehicle may be received. This data corresponds to a plurality of observations including observed positions of a passenger of the vehicle as the passenger approached the vehicle at a first location. The data may be used to determine an observed distance traveled by a passenger to reach a vehicle. A road edge distance between an observed position of an observation of the plurality of observations and a nearest road edge to the observed position may be determined. An inconvenience value for the first location may be determined using the observed distance and the road edge distance. The map data is then generated using the inconvenience value.

Inconvenience for passenger pickups and drop offs for autonomous vehicles

Aspects of the disclosure relate to arranging a pick up and drop off locations between a driverless vehicle and a passenger. As an example, a method of doing so may include receiving a request for a vehicle from a client computing device, wherein the request identifies a first location. Pre-stored map information and the first location are used to identify a recommended point according to a set of heuristics. Each heuristic of the set of heuristics has a ranking such that the recommended point corresponds to a location that satisfies at least one of the heuristics having a first rank and such that no other location satisfies any other heuristic of the set of heuristics having a higher rank than the first rank. The pre-stored map information identifying a plurality of pre-determined locations for the vehicle to stop, and the recommended point is one of the plurality of pre-determined locations. The recommended point is then provided the client computing device for display on a display of the client computing device with a map.

Arranging passenger pickups for autonomous vehicles

Systems and methods for performing operations based on LIDAR communications are described. An example device may include one or more processors and a memory coupled to the one or more processors. The memory includes instructions that, when executed by the one or more processors, cause the device to receive data associated with a modulated optical signal emitted by a transmitter of a first LIDAR device and received by a receiver of a second LIDAR device coupled to a vehicle and the device, generate a rendering of an environment of the vehicle based on information from one or more LIDAR devices coupled to the vehicle, and update the rendering based on the received data. Updating the rendering includes updating an object rendering of an object in the environment of the vehicle. The instructions further cause the device to provide the updated rendering for display on a display coupled to the vehicle.

Lidar based communication

Aspects of the disclosure relate to a method of managing a fleet of autonomous vehicles providing trip services. The method includes receiving information identifying an intermediate destination and a final destination for a trip. In this example, the intermediate destination is a destination where an autonomous vehicle will drop off and wait for a passenger in order to continue the trip, and the final destination is a destination where the trip ends. The method also includes determining an amount of waiting time the vehicle is likely to be waiting for the passenger at the intermediate destination, determining how a vehicle of the fleet of autonomous vehicles should spend the amount of waiting time, and sending an instruction to the vehicle, based on the determination of how the vehicle should spend the amount of waiting time.

Multiple destination trips for autonomous vehicles

The technology involves communicating the reachability status associated with an autonomous vehicle to a user such as a rider within the vehicle, a person awaiting pickup, or a customer that scheduled a package deliver. Reachability information about pickup and/or drop off locations is presentable via an app on a user device, which helps set expectations with customers about where the vehicle is most likely to be able to perform a pickup and/or drop off. This may include indicating how much variance there may be based on current congestion, parking or idling regulations, or weather conditions. The reachability information may be presented via one or more visualization tools to indicate the uncertainty and/or likely final location. Presenting such contextual information may be done based on real time information, and the presentation may be updated as needed. Historical information about the location may also be used to lower the level of uncertainty.

Reachability user experience interfaces for autonomous vehicles

Aspects of the disclosure relate to evaluating quality of a predetermined pullover location for an autonomous vehicle. For instance, a plurality of inputs for the predetermined pullover location may be received. The plurality of inputs may each include a value representative of a characteristic of the predetermined pullover location. The plurality of inputs may be combined to determine a pullover quality value for the predetermined pullover location. The pullover quality value may be provided to a vehicle in order to enable the vehicle to select a pullover location for the vehicle.

Quality scoring for pullovers for autonomous vehicles

Aspects of the disclosure relate to generating a model to assess maximum numbers of concurrent trips for an autonomous vehicle transportation service. For instance, historical trip data, including when requests for assistance were made, response times for those requests for assistance, and a number of available resources when each of the requests for assistance were made may be received. In addition, a number of concurrent trips, or trips that overlap in time, occurring when each of the requests for assistance were made may be received. The model may be trained using the historical trip data and the numbers of concurrent trips. The model may be configured to provide a maximum number of concurrent trips given a period of time, a number of available resources, and a response time requirement.

Resource allocation for an autonomous vehicle transportation service

Aspects of the disclosure relate to providing transportation services with autonomous vehicles. For instance, a first route to a first destination may be determined. The first route may have a first cost. Weather information for the first destination may be received. A characteristic is determined based on the weather information. A second destination having the characteristic may be selected. The second destination may be different from the first destination. A second route to the second destination may be determined. The second route may have a second cost. The first cost may be compared to the second cost, and the vehicle may use the comparison to set one of the first destination or the second destination as a current destination for a vehicle to cause the vehicle to control itself in an autonomous driving mode to the current destination.

Leveraging weather information to improve passenger pickup and drop offs for autonomous vehicles

Aspects of the disclosure relate to enabling roadside assistance to a vehicle that requires assistance having an autonomous driving mode. For instance, a technician may be assigned to the vehicle that requires assistance. A signal corresponding to user input at a remote computing device requesting a change to a state of the vehicle may be received. The signal may be based on details of the assigned technician. Based on the validation, an instruction may be sent to the vehicle to change the state of the vehicle.

Roadside assistance for autonomous vehicles

Aspects of the disclosure provide for advanced trip planning for an autonomous vehicle service. For instance, an example method may include determining a potential pickup location for a user, determining a set of potential destination locations for a user, and determining a set of potential trips. For each potential trip a vehicle of a fleet of autonomous vehicles of the service may be assigned and trip information, including an estimated time of arrival for the assigned vehicle of the potential trip to reach the destination location of the potential trip, may be determined. The trip information for each potential trip may be provided for display to the user. Thereafter, confirmation information identifying one of the set of potential trips may be received, and the assigned vehicle for one first of the set of potential trips may be dispatched to pick up the user.

Advanced trip planning for autonomous vehicle services

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Palo Alto, CA, United States of America

Salil Pandit