Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Jidosha Kabushiki Kaisha

Kabushiki Kaisha Toyota Chuo Kenkyusho

Tomoki Nishi

Masanori Ishigaki

Shuji Tomura

Katsuhiro Sakai

Michael R James

Bunyo Okumura

Shigeaki Goto

Koji Yamada

Hiroyuki Yamada

Marie Ishikawa

George C Bucsan

Akinori Sato

Akira Shichi

Makoto Kusakabe

Satoshi Iwai

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.

System, and method for swapping self driving energy module for an energy management system

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.

Storage battery module rental system, rental method, and rental program

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.

Autonomous operation for an autonomous vehicle objective in a multi-vehicle environment

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.

Autonomous operation capability configuration for a vehicle

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.

Online learning and vehicle control method based on reinforcement learning without active exploration

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.

Control policy learning and vehicle control method based on reinforcement learning without active exploration

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.

Growing community of inventors

Ann Arbor, MI, United States of America

Tomoki Nishi