Abstract: A controller is provided that is configured to execute: predicting a distribution of first users in a predetermined area; predicting, based on the predicted distribution of the first users, a first point that is a point where a density of the first users exceeds a predetermined upper limit value in the predetermined area in the future; and transmitting, to a terminal of a second user who is a provider of a service, information about prompting provision of the service at a second point other than the first point in the predetermined area.

Abstract: A controller of an information processing apparatus disclosed obtains first information about a tendency of move of a user staying in a specific facility. The controller forecasts, on the basis of the first information, an expected destination of move to which the user will move from the specific facility and an expected time slot of move during which the user will start to move from the specific facility to the expected destination of move. The controller calculates the number of users who are expected to move from the specific facility to the expected destination of move in each expected time slot of move on a slot-by-slot basis.

Abstract: A system includes one or more processors configured to: acquire request information including a desired date and time, a point of departure, and a destination, from a user placing a reservation for use of a moving body; derive a reservation result indicating whether the reservation is completed for the request information; derive, based on the request information, the reservation result indicating completion, suspension, or failure for the request information once or a plurality of times; derive, at a first time of deriving the reservation result, one of reservation results including at least the reservation result indicating the completion and the reservation result indicating the suspension for the request information; derive the reservation result indicating the completion or the failure by a predetermined deadline for the request information for which the reservation result indicates the suspension; and notify the user of the reservation result that has been derived.

Abstract: When an information processing apparatus disclosed receives a signal requesting dispatch of a taxi with information about a desired location of getting-in, it determines a recommended location of getting-in on the basis of at least one of information about a physical condition relating to a road in a specific area including the desired location of getting-in, information about instances of users' getting in and/or getting out of taxis that took place in the specific area in the past, and information about a condition of the road in the specific area. The information processing apparatus sends information for inviting a user to wait at the recommended location of getting-in determined as above to a user's terminal used by the user.

Abstract: A controller is provided that is configured to execute: predicting a distribution of first users in a predetermined area; predicting, based on the predicted distribution of the first users, a first point that is a point where a density of the first users exceeds a predetermined upper limit value in the predetermined area in the future; and transmitting, to a terminal of a second user who is a provider of a service, information about prompting provision of the service at a second point other than the first point in the predetermined area.

Abstract: A controller of an information processing apparatus disclosed obtains first information about a tendency of move of a user staying in a specific facility. The controller forecasts, on the basis of the first information, an expected destination of move to which the user will move from the specific facility and an expected time slot of move during which the user will start to move from the specific facility to the expected destination of move. The controller calculates the number of users who are expected to move from the specific facility to the expected destination of move in each expected time slot of move on a slot-by-slot basis.

Abstract: A system includes energy modules that output power to an energy management bus based on load demands. An energy module includes energy cells enclosed within a module housing that provide power to the energy management bus and a driving system attached to the module housing that transports the energy module. The energy module includes a local controller that controls power output from the energy cells to the energy management bus, engages a self-driving mode in response to receiving a disconnection signal from a central controller, and controls movement of the energy module in the self-driving mode to a predetermined location via the driving system. The central controller receives a current module status from the energy modules and controls a configuration of the energy modules providing power to the energy management bus based on the current module status.

Abstract: A storage battery module rental system includes a storage battery module prepared in each providing point that is dispersedly distributed and that can be rented to a user, an acquisition unit configured to acquire individual information of a plurality of the users and module information from the storage battery module, the user being a rentee candidate of the storage battery module, a determining unit configured to determine an incentive given for renting the storage battery module based on the individual information and the module information in such a way that the incentive could differ for each user; and a notifying unit configured to notify the user of the incentive determined by the determining unit for each of the users.

Abstract: A storage battery module rental system includes a storage battery module prepared in each providing point that is dispersedly distributed and that can be rented to a user, an acquisition unit configured to acquire individual information of a plurality of the users and module information from the storage battery module, the user being a rentee candidate of the storage battery module, a determining unit configured to determine an incentive given for renting the storage battery module based on the individual information and the module information in such a way that the incentive could differ for each user; and a notifying unit configured to notify the user of the incentive determined by the determining unit for each of the users.

Abstract: A device and method for state progression in a set of autonomous vehicle states related to an autonomous vehicle objective for a multi-vehicle environment. A vehicular state of another vehicle of the multi-vehicle environment is discerned to produce a discerned vehicular state of the another vehicle, wherein the another vehicle operates to affect the autonomous vehicle objective. An adaptive policy is formed for a present one of the set of autonomous vehicle states based on the discerned vehicular state, and producing from the adaptive policy an autonomous vehicle action to progress to a subsequent one of the set of autonomous vehicle states. The autonomous vehicle action is transmitted for effecting a progression to the subsequent one of the set of autonomous vehicle states.

Abstract: A method and device for configuring an autonomous operation capability of a vehicle are disclosed. A first dataset is identified relating to a plurality of vehicles, wherein the vehicle is one of the plurality of vehicles. A state value function is produced for the autonomous vehicle capability in relation to the first dataset. A second dataset is identified relating to a portion of the plurality of vehicles, wherein the portion of the plurality of vehicles includes the vehicle. A policy control gain is optimized for the autonomous vehicle capability in relation to the second dataset. The autonomous vehicle capability is operable to generate an autonomous vehicle action for progressing to a next state based on the state value function in cooperation with the policy control gain.

Abstract: A system includes energy modules that output power to an energy management bus based on load demands. An energy module includes energy cells enclosed within a module housing that provide power to the energy management bus and a driving system attached to the module housing that transports the energy module. The energy module includes a local controller that controls power output from the energy cells to the energy management bus, engages a self-driving mode in response to receiving a disconnection signal from a central controller, and controls movement of the energy module in the self-driving mode to a predetermined location via the driving system. The central controller receives a current module status from the energy modules and controls a configuration of the energy modules providing power to the energy management bus based on the current module status.

Abstract: A method and device for configuring an autonomous operation capability of a vehicle are disclosed. A first dataset is identified relating to a plurality of vehicles, wherein the vehicle is one of the plurality of vehicles. A state value function is produced for the autonomous vehicle capability in relation to the first dataset. A second dataset is identified relating to a portion of the plurality of vehicles, wherein the portion of the plurality of vehicles includes the vehicle. A policy control gain is optimized for the autonomous vehicle capability in relation to the second dataset. The autonomous vehicle capability is operable to generate an autonomous vehicle action for progressing to a next state based on the state value function in cooperation with the policy control gain.

Abstract: A device and method for state progression in a set of autonomous vehicle states related to an autonomous vehicle objective for a multi-vehicle environment. A vehicular state of another vehicle of the multi-vehicle environment is discerned to produce a discerned vehicular state of the another vehicle, wherein the another vehicle operates to affect the autonomous vehicle objective. An adaptive policy is formed for a present one of the set of autonomous vehicle states based on the discerned vehicular state, and producing from the adaptive policy an autonomous vehicle action to progress to a subsequent one of the set of autonomous vehicle states. The autonomous vehicle action is transmitted for effecting a progression to the subsequent one of the set of autonomous vehicle states.

Abstract: A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

Abstract: A computer-implemented method is provided for autonomously controlling a vehicle to perform a vehicle operation. The method includes steps of applying a passive actor-critic reinforcement learning method to passively-collected data relating to the vehicle operation, to learn a control policy configured for controlling the vehicle so as to perform the vehicle operation with a minimum expected cumulative cost; and controlling the vehicle in accordance with the control policy to perform the vehicle operation.

Abstract: A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

Abstract: A computer-implemented method is provided for autonomously controlling a vehicle to perform a vehicle operation. The method includes steps of applying a passive actor-critic reinforcement learning method to passively-collected data relating to the vehicle operation, to learn a control policy configured for controlling the vehicle so as to perform the vehicle operation with a minimum expected cumulative cost; and controlling the vehicle in accordance with the control policy to perform the vehicle operation.

Abstract: This application describes an automated driving system and methods. The automated driving system includes a perception system disposed on an autonomous vehicle. The automated driving system can detect, using the perception system, information for an environment proximate to the autonomous vehicle. The information for the environment includes current behavior of an object of interest and traffic density information. The automated driving system can also determine a classifier for the environment based on a prediction of future behavior of the object of interest. Based on the classifier, the automated driving system can identify a transition between vehicle states, the vehicle states being associated with a planned action for the autonomous vehicle. The automated driving system can also send a command to one or more vehicle systems to control the autonomous vehicle to execute the planned action according to the transition.