CPC Class G05B

An apparatus includes: a first sensor configured to provide a first input; a second sensor configured to provide a second input; and a processing unit configured to receive the first input from the first sensor, and a second input from the second sensor; wherein the processing unit is configured to determine a first probability of a first predicted event, and a second probability of a second predicted event, wherein the first predicted event and the second predicted event are associated with an operation of the vehicle; and wherein the processing unit is configured to calculate a risk score based on the first probability of the first predicted event, and based on the second probability of the second predicted event.

The present application pertains to the technical field of vehicles, and in particular to a vehicle intelligent control system based on a dual independent control system and a corresponding vehicle. The system includes a locomotive terminal and a cloud server. The locomotive terminal includes a first control system and a second control system both being operable independently and capable of exchanging data with each other in real time. The first control system monitors an operation state of the vehicle in real time and transmits at least part of operation state data to the second control system in real time, and controls an operation of the vehicle according to an internal preset instruction and a control instruction received from the second control system. The second control system receives data transmitted by the first control system and transmits at least part of the received data to the cloud server, and receives a control instruction from the cloud server and forwards the control instruction for controlling the operation of the vehicle to the first control system. The present application improves the adaptability and stability of the system, enhances the user experience and increases the safety of the vehicle.

A mobile object control device acquires, as learning history data, driving history data obtained when a learning mobile object is operated in a risk-free environment; performs learning for imitating driving of the learning mobile object in the risk-free environment using the learning history data as training data and generates an imitation learning model; acquires, as training history data, driving history data obtained when a mobile object for generating training data is operated in the same environment as the environment in which the learning history data has been acquired; estimates whether the training history data matches the learning history data using the training history data as input to the imitation learning model and assigns a label related to risks; and infers a result for controlling a mobile object to be controlled using at least the label related to risks as training data, based on sensor information of the mobile object.

A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.

A kitting machine is disclosed. In various embodiments, the machine includes an actuator configured to move a surface or receptacle to a position associated with item retrieval; and a controller configured to control operation of the actuator to position an item in the position associated with item retrieval, in a manner that is synchronized at least in part with operation of a robotic retrieval device configured to retrieve the item from the position associated with item retrieval.

An article processing apparatus (1) according to an embodiment discharges an article to a downstream apparatus (2) disposed downstream. The article processing apparatus includes: a display (110); a capturing unit (100) that captures a picture including at least part of the downstream apparatus; and a controller (30) that displays, in a case where a processing operation of the article remains stopped in the article processing apparatus, on the display, the picture captured by the capturing unit.