A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

Dynamic allocation and coordination of auto-navigating vehicles uses robotic vehicles and centrally dispatched roaming order selectors to create a significantly more efficient, yet flexible, approach to picking goods within a warehouse. Robotic vehicles are configured to be loaded with goods from pick faces to fill orders. Each robotic vehicle follows a route that includes appropriate pick face locations. The robotic vehicles navigate from pick face to pick face where particular goods are located. Order selectors are dynamically and independently dispatched to meet the robotic vehicles at their pick face locations to load goods. Movement of the order selectors is orchestrated to increase efficiency in the order filling process within the warehouse.

A system and method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle is described. In one embodiment, a method for controlling an autonomous vehicle to affect a view seen by an occupant of the autonomous vehicle includes determining a navigation route, determining content associated with the navigation route, monitoring current conditions of the autonomous vehicle and the occupant, determining, based on the current conditions, whether to change a position of the vehicle to affect the view seen by the occupant, and when the current conditions permit, moving the autonomous vehicle to affect the view seen by the occupant.

There is provided a cargo transport system including: a management device; and a plurality of automated guided vehicles. Each of the automated guided vehicles includes: a movement instruction receiving unit that receives, from the management device, a movement instruction for moving to at least one loading operation place where a loading operation of loading a cargo to be transported into the automated guided vehicle is performed or a specified position near the loading operation place; an information transmitting unit that transmits, to the management device, load information concerning a load state of the cargo loaded into the automated guided vehicle; and a traveling unit that performs automated traveling in accordance with the received movement instruction. The management device includes: a movement instruction management unit that transmits the movement instruction to each of the automated guided vehicles; a load information receiving unit; and a load amount management unit.

In a method for managing compensatory transactions between vehicles, a compensation engine determines relative priorities of the first and second vehicles. The compensation engine determines compensation between the first and second vehicles based on the relative priorities of the first and second vehicles. Following the determination of compensation, the compensation engine executes a transfer of compensation between the first and second vehicles.

A method of using robotic units to provide security for a site, the method comprising: creating a digital twin of the site; using a pathing engine to model and determine possible robotic unit paths around the site; and using the digital twin and possible robotic unit paths to create numerous permutations of a security mission plan for the site.

A transportation system controls a fleet of autonomous vehicles to implement passenger transportation and coordinate delivery of baggage or other associated items using separate vehicles. The transportation system receives passenger data and associated item data via a user interface, and determines the number and type of autonomous vehicles to transport the passengers and items from selected pick-up locations to a destination. In various implementations, the transportation system may support different pick-up locations, pick-up times and/or delivery times for the passengers and associated items. The transportation system also may determine delayed item delivery options for different delivery times and modes of transportation. Based on the passenger and item data, along with input received via the user interface, the transportation system determines the vehicles to deploy and the delivery routes, and transmits instructions to the autonomous vehicles to provide the passenger transportation and perform the item delivery.

A robot management system executes, for a plurality of transport robots, an estimation process for estimating load applied to the transport robots based on a current value during traveling and a traveling distance or a traveling time of the transport robots. The robot management system determines a transport robot to be used from among the transport robots based on an estimation result in the estimation process.

The present teaching relates to method, system, medium, and implementation of in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are acquired continuously via a plurality of types of sensors deployed on the vehicle, where the plurality of types of sensor data provide information about surrounding of the vehicle. One or more items surrounding the vehicle are tracked, based on some models, from a first of the plurality of types of sensor data from a first type of the plurality of types of sensors. A second of the plurality of types of sensor data are obtained from a second type of the plurality of sensors and are used to generate validation base data. Some of the one or more items are labeled, automatically, via validation base data to generate labeled at least some item, which is to be used to generate model updated information for updating the at least one model.

An apparatus is presented that receives first and second parameters including first parameters from plural harvesting machines, determines a batch unload for a specified quantity of material to unload from each of the harvesting machines, requests permission to receive the batch unload from one of the harvesting machines, and communicates a control signal to trigger the requested batch unload from the one of the harvesting machines.