Waymo LLC

Teradata Us, Inc.

Aster Data Systems, Inc.

Eric Friedman

Peter Michal Pawlowski

Cameron Blume

Gaurav Agarwal

Pawel Fedorynski

Salil Pandit

Matthew Corey Hall

Mishika Vora

Jonathan Lee Pedersen

Donghyun Kim

Bruce Mai

Saksiri Tanphaichitr

Daniella Gutlansky

Samrat Kansara

Ben Qingzhou Wang

Andrew Chan

Eamonn Michael Doherty

Rangarajan Vasudevan

Jia-Yu Aw

Prasan Roy

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

The technology relates to a seamless interface between an autonomous vehicle service provider and one or more ridesharing or ride-hailing partner companies. An API enables efficient and robust communication with such partner companies for effective rider services and support. The API enables a partner company to determine how quickly an autonomous vehicle from the service provider could pick up a rider from a given location, as well as the cost of the ride. Customers may request rides or modify scheduled trips via an app running on the user's mobile phone or other communication device. The service may allow customers to directly communicate with the ride provider company, and may also allow customers to book or modify rides via interaction with a partner company that works with the ride provider company, either with the same or a different app.

Partner trip application programming interface

The technology relates to a seamless interface between an autonomous vehicle service provider and one or more ridesharing, ride-hailing or delivery partner companies, in order to provide timely, efficient rider or delivery services and support. A partner trip application programming interface (API) provides robust features to the ridesharing or ride-hailing partner companies and also supports delivery of meals, groceries, packages or other cargo. For instance, the API may be employed with delivery partners (e.g., food, package or bulk cargo delivery), and/or for concierge services (e.g., a hotel or medical provider or an e-commerce specialist that coordinates deliveries). The technology supports both ad hoc and scheduled trips. Agents for different partners may schedule trips to or from specific stores or other (geofenced) locations, and observe and manage trips for an entire enterprise. Permissions can be tailored for each agent.

Dispatch and local delivery interface for autonomous vehicles

An example method includes setting an initial pickup location. The method includes causing an autonomous vehicle to navigate towards the initial pickup location. The method includes periodically generating a plurality of candidate updated pickup locations. The method includes selecting a suggested pickup location from the plurality of candidate pickup locations that achieves a benchmark for reducing one or more of a time and a distance associated with the initial pickup location for one or more of the autonomous vehicle and the client device. The method includes determining that the suggested pickup location satisfies a set of pre-update checks for limiting a number of pickup location updates. The method includes responsive to (i) selecting the suggested pickup location, and (ii) determining that the suggested pickup location satisfies the set of pre-update checks, causing the autonomous vehicle to navigate to the suggested pickup location.

Systems and methods for updating pickup and dropoff locations for a vehicle

A system and method of massively parallel data processing are disclosed. In an embodiment, a method includes generating an interpretation of a customizable database request which includes an extensible computer process and providing an input guidance to available processors of an available computing environment. The method further includes automatically distributing an execution of the interpretation across the available computing environment operating concurrently and in parallel, wherein a component of the execution may be limited to at least a part of an input data. The method also includes automatically assembling a response using a distributed output of the execution.

System and method of massively parallel data processing

Several methods and a system for analyzing ordered data using pattern matching over an indefinitely long ordered sequence of rows in a relational database are disclosed. In one embodiment, a method of a server includes receiving an ordered data in a relational database. The method further includes matching a pattern specified in a query on ordered data in a relational database in a single pass in constant space for overlapping mode of results. The method also includes creating an output data in the single pass in constant space for overlapping mode of results based on the matching of the ordered data with the pattern in the relational database query.

Method and system for analyzing ordered data using pattern matching in a relational database

Growing community of inventors

Mountain View, CA, United States of America

Eric Friedman