The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

A method for determining the speed of a vehicle is described. In this method, at least one object present in the environment of the vehicle is detected and a relative speed of the detected object in relation to the vehicle is measured. In addition, the speed of the vehicle is determined on the basis of the relative speed of the object.

A method for determining the speed of a vehicle is described. In this method, at least one object present in the environment of the vehicle is detected and a relative speed of the detected object in relation to the vehicle is measured. In addition, the speed of the vehicle is determined on the basis of the relative speed of the object.

A system for performing track maintenance operations is described. The system includes a lead vehicle for identifying sections of rail that have been pre-marked for track maintenance operations. The lead vehicle further includes a control system for receiving and transmitting coordinates of the pre-marked track sections. The system further includes at least one drone vehicle for receiving the coordinates from the control system and the drone vehicle has at least one workhead for performing track maintenance operations on the pre-marked track sections.

A system for performing track maintenance operations is described. The system includes a lead vehicle for identifying sections of rail that have been pre-marked for track maintenance operations. The lead vehicle further includes a control system for receiving and transmitting coordinates of the pre-marked track sections. The system further includes at least one drone vehicle for receiving the coordinates from the control system and the drone vehicle has at least one workhead for performing track maintenance operations on the pre-marked track sections.

According to various aspects, exemplary embodiments are disclosed herein of wireless locomotive emergency stop systems. Also disclosed are exemplary methods of providing locomotives with wireless emergency stop systems, and exemplary methods of operating wireless locomotive emergency stop systems.

In various example embodiments, a method and system for assessment of wind-induced train blow-over risk are presented.

The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

An automatic train operation system includes a first control system configured to run a first software for controlling a first vehicle subsystem and a second control system configured to run a second software for controlling a second vehicle subsystem. The automatic train operation system also includes a software verification controller. The software verification controller is configured to identify a first identifier of the first software and a second identifier of the second software as a software configuration and determine whether the software configuration is preapproved. The software verification controller is also configured to, if the software configuration is preapproved, authorize the first control system and the second control system to run the first and second software.