Hitachi, Ltd.

Hitachi Automotive Systems, Ltd.

Heming Chen

Sujit S Phatak

Yuan Xiao

Donald James McCune

Can Wang

Yi-Chu Chang

Sanketh Shetty

Yasuhiro Ito

Prashanth Avireddi

Ichiki Homma

Nikhil Seera

Tianye Ma

Jia Li

Example implementations described herein involve systems and methods for operation of a robot configured to work on a first process and a second process, which can involve receiving sensor data indicative of a status of one or more of the first process and the second process; for the status indicative of the first process waiting on the robot, controlling the robot to work on the first process; and for the status indicative of the first process not waiting on the robot, controlling the robot to conduct one or more of work on the second process or return to standby.

Autonomous task management industrial robot

Example implementations described herein are directed to a simulation environment for a real world system involving one or more robots and one or more sensors. Scenarios are loaded into a simulation environment having one or more virtual robots corresponding to the one or more robots, and one or more virtual sensors corresponding to the one or more virtual system to train a control strategy model from reinforcement learning, which is subsequently deployed to the real world environment. In cases of failure of the real world environment, the failures are provided to the simulation environment to generate an updated control strategy model for the real world environment.

Self-learning industrial robotic system

Example implementations described herein are directed to systems and methods that include the storage of I/F communication activity during a co-execution and a repeater to reproduce such I/F communication activity. Thus in a subsequent re-execution of the simulation or applications, one or more of the simulations or applications utilized can be replaced with a repeater without requiring the full execution of the simulation or application, thereby saving license usage as well as requiring fewer hardware resources for execution.

Co-simulation repeater with former trace data

Example implementations described herein facilitate an interactive environment for companies and personals to validate and develop autonomous driving systems. Such implementations apply to, but are not limited to, applications such as sensor data collection for deep learning model training; validation and development of various detection algorithms; sensor fusion (e.g., radar, lidar, camera) algorithm development and validation, trajectory/motion planning algorithm validation; and control algorithm validation.

Online self-driving car virtual test and development system

In some examples, a system may receive a plurality of parameters for a filter design, including a noise parameter. The system may determine a plurality of candidate filter configurations based on at least one of the received parameters. The system may further determine, for each candidate filter configuration of the plurality of candidate filter configurations, based on a trained machine learning model, an estimated electromagnetic interference (EMI) noise associated with each candidate filter configuration. The system may select at least one of the candidate filter configurations based on the estimated EMI noise. In some cases, the system may perform a simulation using the selected candidate filter configuration. Based on the results of the selecting and/or the simulation, the system may send information related to the at least one selected candidate filter configuration to a computing device.

Automated electromagnetic interference filter design

In some examples, a system may receive, over a network from a vehicle computing device onboard a vehicle, sensor data for at least one sensed parameter of a vehicle component. The system may determine, based on the sensor data, a damage result indicative of fatigue damage to the vehicle component. Based at least partially on the damage result, the system may send a communication to at least one of the vehicle computing device onboard the vehicle, or a computing device associated with an account associated with the vehicle. In some cases, the damage result may be determined from at least one of accessing a lookup table using the sensor data, or executing a fatigue simulation using sensor data.

Vehicle component failure prevention

In some examples, a processor on a vehicle may determine, based at least partially on information obtained from a sensor, that a fuel level of the vehicle is increasing. Further, the processor may determine that the fuel level has stabilized for a threshold time. In addition, the processor may determine an amount of fuel added to the vehicle, and may send, over a wireless network to a computing device, a communication indicating the amount of fuel added. In some cases, the computing device may compare the amount of fuel indicated in the communication from the processor with an amount of fuel indicated in information received from a point-of-sale device for determining a difference.

Fuel measurement for fleet vehicles

The driver's driving behavior will be recorded and a signature will be created either on vehicle or on a cloud data center. The driver signature can be accessed using secure authentication by any vehicle he will be driving. The data collection is a continuous process monitoring driving behavior of the driver. The guidelines from OEM are used to compare driver signature with ideal driving characteristics. Changes are pushed to the vehicle controller to modify controls of individual automotive components to adjust the efficiency of vehicle and improve the ride comfort for the driver. The changes to be made can be decided on a remote cloud system or on the vehicle.

Active vehicle performance tuning based on driver behavior

Apparatus for providing real-time data to a hardware-in-the-loop simulator for an automotive vehicle. The apparatus includes a test vehicle having at least one sensor which generates an output signal representative of a condition of the vehicle. A wireless transmitter such as a cellular phone on the motor vehicle receives the sensor output signal as an input signal and transmits that output signal to a computer network. A simulator data server receives the data from the computer network and provides that data to the hardware-in-the-loop simulator.

Apparatus for providing data to a hardware-in-the-loop simulator

A system and method for allocating software resources. Multiple tasks are received from a network in which each task requires at least one software resource. Each task is analyzed to determine the type of resource(s) required to execute each such task. The availability of the software resource(s) is determined and, if available, allocated to the requesting task. If the software resource(s) is not available, the task is stored in a queue until the software resource(s) becomes available.

Virtual resource management tool for cloud computing service

A control system for a vehicle having a plurality of subsystems in which each subsystem includes a sensor and/or actuator. A general purpose processor is programmed with a plurality of software applications in which each software application is associated with one of the vehicle subsystems. The subsystems and processor communicate with each other through an electrical bus and each subsystem includes a communication interface to receive commands from the processor as well as to send task signals to the processor. A task arbitrator prioritizes the order of execution of multiple task signals received by the processor during a preset time period. The general purpose processor then initiates execution of the software application associated with the subsystem of the sensor signal received from the task arbitrator and thereafter transmits a control signal to one or more of the subsystems via the bus as a result of execution of the associated software application.

Control system for an automotive vehicle

A method for identifying a value of an unknown circuit component for an analog signal having a known output profile in which a simulation list of the analog circuit is first created including the component with the unknown value. A transfer function for the known output value is then created using a programmed processor and the transfer function is then solved by the processor for the value of the unknown component. For nonlinear circuit components, a linear model is substituted for the nonlinear components prior to creating the simulation list.

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Novi, MI, United States of America

Heming Chen