Uber Technologies, Inc.

Uatc, LLC

Carnegie Mellon University

International Truck Intellectual Property Company, LLC

Aurora Operations, Inc.

Neil Stegall

Brent Justin Goldman

Noah Zych

Christopher Matthew D'Eramo

Leigh Hagestad

Min Kyu Park

Rei Chiang

Ye Yuan

Kane Sweeney

Valerie Nina Chadha

Herman Herman

James Andrew Bagnell

Christopher Chandler Fromme

Jaime W Bourne

Richard Pantaleo

Joan Campoy

Raphael Rogman

Elliot Allen Cuzzillo

Caitlin Travers

Jeffrey David McMahill

Aubrey C Donnellan

Victor Soto

A network computing system can coordinate on-demand transport serviced by transport providers operating throughout a transport service region. The transport providers can comprise a set of internal autonomous vehicles (AVs) and a set of third-party AVs. The system can receive a transport request from a requesting user of the transport service region, where the transport request indicates a pick-up location and a destination. The system can determine a subset of the transport providers to service the respective transport request, and executing a selection process among the subset of the transport providers to select a transport provider to service the transport request. The system may then transmit a transport assignment to the selected transport provider to cause the selected transport provider to service the transport request.

On-demand transport selection process facilitating third-party autonomous vehicles

A computing system can maximize throughput for a common rendezvous location by determining estimated times of arrival (ETAs) to the common rendezvous location for matched users and/or transport providers. Based on the ETAs of each of the transport providers, the computing system can generate a dynamic queue comprising the transport providers for the common rendezvous location and manage the dynamic queue by routing the transport providers through the common rendezvous location. The computing system can further dynamically adjust the queue based on changes to the ETAs by transmitting updated navigation-related data to one or more of the matched transport providers.

Coordinating transport through a common rendezvous location

Systems and methods for automatically adjusting the interior cabin of an autonomous vehicle are provided. In one example embodiment, an autonomous vehicle can include a main body including a floor and a ceiling that at least partially define an interior cabin of the autonomous vehicle. The autonomous vehicle can include a partition wall that is movable within the interior cabin of the autonomous vehicle. The partition wall can extend between the floor to the ceiling of the main body. The autonomous vehicle can include a computing system configured to receive data indicative of one or more service assignments associated with the autonomous vehicle and to adjust a position of the partition wall within the interior cabin based at least in part on the one or more service assignments.

Automatically adjustable partition wall for an autonomous vehicle

Systems and methods for evaluating and deploying fleets of autonomous in operational domains are described. A computing system may obtain data indicative of one or more capabilities of at least one autonomous vehicle, data indicative of vehicle service dynamics in an operational domain over a period of time, and determining a plurality of resource performance parameters respectively for a plurality of autonomous vehicle fleets associated with potential deployment in the operational domain. Each autonomous vehicle fleet can be associated with a different number of autonomous vehicles The resource performance parameter for each autonomous vehicle fleet can be based at least in part on the one or more capabilities of the at least one autonomous vehicle and the vehicle service dynamics in the operational domain. The computing system can initiate an action associated with the operational domain based at least in part on the plurality of resource performance parameters.

Autonomous vehicle fleet management for improved computational resource usage

A self-guided blossom picker uses a vision system to identify and locate blossoms or inflorescence growing on a plant. The device can be towed by a tractor or it can be self-propelled. Image data captured by the vision system is sent to an machine vision module, which interprets the data and identifies a location of blossom. A controller uses the location data to command a picker to the proper location. A cutter on the picker is actuated to remove the blossom.

Self-guided blossom picking device

A network computing system can maximize throughput for a common rendezvous location by determining estimated times of arrival (ETAs) to the common rendezvous location for matched users and/or the transport providers. Based on the ETAs of each of the transport providers, the computing system can generate a dynamic queue comprising the transport providers for the common rendezvous location, and manage the dynamic queue by sequentially routing the transport providers through the common rendezvous location. The computing system can further dynamically adjust the queue based on changes to the ETAs.

A truck cab has a side door () via which a person enters and exits the cab by ascending and descending steps () on a side of the truck. At least one of the steps is selectively positionable to a deployed position for use by a person ascending or descending the steps and to a non-deployed position when the truck is to be driven. The deployed position is relatively more outboard on the side of the truck than the non-deployed position.

Ingress/egress steps for a highway truck

Growing community of inventors

Pittsburgh, PA, United States of America

Neil Stegall