A train control system uses artificial intelligence for maintaining synchronization between centralized and distributed train control models. A machine learning engine receives training data from a data acquisition hub, a first set of output control commands from a centralized virtual system modeling engine, and a second set of output control commands from a distributed virtual system modeling engine. The machine learning engine compares the first set of output control commands and the second set of output control commands, and trains a learning system using the training data to enable the machine learning engine to safely mitigate any difference between the first and second sets of output control commands using a learning function including at least one learning parameter.

A method for reducing slack in a linkage chain of a vehicle truck assembly is provided. In one example, the method includes responding to a request to de-lift a lift mechanism by reducing pressure in an actuator coupled to the lift mechanism, where the lift mechanism is configured to transfer a load from a first axle to a second axle of the vehicle during the de-lift, and during the de-lift, maintaining the pressure in the actuator at or above a threshold pressure to maintain tension in a weight transfer device of the lift mechanism

This system includes a ground ATC and an on board ATC, which is switched from an active mode toward a standby mode and vice versa by a wake-up unit. In the standby mode, only the following components remain powered: odometry device; a main computer; a radio communication device between the on board ATC and the ground ATC; the wake-up unit. The main computer is programmed so as, in the standby mode, to verify that the movement of the train measured by the odometry device from the switching from the active mode to the standby mode is zero and, in the affirmative, to send the ground ATC an instantaneous position of the train using the radio communication device.

A hybrid power supply system of diesel multiple unit is disclosed. When a train is running, an energy management module sends a level signal of a master controller of the train to an inverter, and the inverter, according to the received level signal of the master controller and the dynamic performance of the hybrid power supply system, sets an envelope curve of train speed vs. traction force and an envelope curve of train speed vs. regenerative braking force to control a traction motor to output the corresponding torque. Further, the inverter, according to the voltage and current values acquired at the input end, calculates and sends a current actual demanded power to the energy management module, the energy management module, according to the current available power of a supercapacitor, calculates a required output power and sends a command of the required output power to a rectifier, and the rectifier, according to the command of the energy management module, controls the internal electric power pack to output corresponding power. The system is simple in structure and reliable in control, and can increase the dynamic performance of the train and improve the transportation capability of the train. A hybrid power supply method for a diesel multiple unit is also disclosed.

A method of controlling a vehicle to soft-land at a target destination includes: determining a terminal set, which is a set of system states of the vehicle under which the vehicle will converge to a target set under constant zero control input; determining a sequence of polytopes, each polytope being an approximation of a backward reachable set of system states of the vehicle; and controlling the vehicle to reach the destination based on the determined polytopes. The method may be performed by one or more controllers.

A robust system processor comprising three parallel processors, each configured to process in parallel an input and emit an output; and a reconciler that compares the three outputs, determines whether at least two of the outputs are equal, and if so validates the majority output and communicates the validated output via a network to at least one other system processor.

An exemplary embodiment of the present disclosure provides a robot comprising a body, a processor, and a memory. The memory further comprising instructions that, when executed by the processor cause the body to engage a first wire to support the robot from the first wire, traverse the first wire, engage a second wire simultaneously while engaging the first wire, the second wire branching from the first wire, then after engaging the second wire, disengage the first wire and traverse the second wire.

The present invention is provided with: a sensing unit which is provided in a vehicle and measures the three-dimensional shape of an object; a matching unit for performing matching between multiple sets of structure information, in which are associated a data group indicating the three-dimensional shapes of structures provided beforehand near a path the vehicle will travel along and positional information indicating the posit ions of the structures, and a data group indicating the three-dimensional shape of the object measured by the sensing unit, and for specifying the position of the object; and an estimated position determination unit for determining, with the position specified by the matching unit as a reference position, the estimated position of the vehicle on the basis of the reference position.

A system and a method for monitoring pressure inside a railway vehicle comprise a carriage pressure detection device, a control device, and an alarm device. The control device is configured to receive and process a pressure signal collected by the carriage pressure detection device, perform calculation on and analyze the collected data, and transmit an alarm signal to the alarm device as an alarm when a preset alarm condition is met. When a pressure protection device fails, pressure changes inside the vehicle are monitored in real time by the carriage pressure detection device functioning independently of the pressure protection device.

Systems and methods for obstacle avoidance with a self-driving vehicle are provided. The system comprises a processor connected to the self-driving vehicle and a sensor in communication with the processor. The sensor is configured to detect objects. The processor is configured to receive a measurement of the self-driving vehicle's speed, and define a sensor region based on the speed. The processor can determine that an object detected by the sensor is within the sensor region, and then initiate a fail-safe routine. The sensor region may be defined based on a range parameter. The sensor region may be defined based on the stopping distance of the vehicle. The sensor region may be redefined when the vehicle's speed changes.