A positive train control may comprise a plurality of different sensors coupled to a processor that determines whether there is an anomaly of a track way, and if there is, provides an alert and/or a train control action. The plural sensors may include a visual imager, an infrared imager, a radar, a doppler radar, a laser sensor, a laser ranging device, an acoustic sensor, and/or an acoustic ranging device. Data from the plural sensors is geo-tagged and time tagged. Some embodiments of the train control employ track monitors, switch monitors and/or wayside monitors, and some employ locating devices such as GPS and inertial devices.

A train wireless system includes a train detecting apparatus on the ground and a controller on a train. The detecting apparatus includes a detector and a calculator. The detector detects that the train is on rails in a block. The calculator measures an on-rail time during which the detector detects the train in the block, and calculates an on-rail detecting time during which the train has been on the rails in the block. The controller includes a distance measurer, a time measurer, a recorder, and a train-length calculator. The distance measurer measures a travelling distance of the train from a beginning of the block, the time measurer measures an elapsed time since the distance measurer starts the measurement, the recorder records the elapsed time and the travelling distance, and the train-length calculator searches the recorder based on the detecting time, and calculates the train length using a selected travelling distance.

This system utilizes autonomous rail cars that automatically couple and uncouple to and from a primary moving train. The primary train travels along a given route but never needs to stop at intermediate stations. Instead, autonomous rail cars gather passengers or goods at designated station locations and then automatically depart in order to meet up with the primary train. When the autonomous rail cars couple to the primary train, the contents of the autonomous rail car are transferred to the primary train. Likewise, contents that are already aboard the primary train which need to depart will be loaded onto this autonomous rail car. When the primary train approaches the next station, the autonomous rail car will detach and stop at the station while the primary train continues to travel on. This process repeats for each station on the route.

In a method for calculating, by an estimator, an instantaneous velocity vector {right arrow over (V.sub.u)} of a rail vehicle, the estimator receives measurements from an inertial unit at a fixed point in the vehicle body and determines a mathematical model M of the dynamics of the vehicle moving on a track, the model being dependent on the bias of the inertial unit and installation parameters, a virtual sensor is determined based on the model M, the virtual sensor enabling calculation, from model parameters, two theoretical transverse velocities δv.sub.y.sub.c, and δv.sub.z.sub.c along axes y.sub.c and z.sub.c, respectively. An iterative estimator calculates {right arrow over (V.sub.u)}, and includes the virtual sensor, the estimator being configured so the two theoretical transverse velocities are zero regardless of the rail configurations, the estimator enabling correction of the biases of the inertial unit and estimate installation parameters. Auxiliary velocity or distance travelled sensors are not used to calculate {right arrow over (V.sub.u)}.

A condition monitoring system for bearing units for vehicles, the system including at least one condition monitoring unit for measuring at least one operating parameter of one bearing unit and a control unit for receiving and processing signals obtained from the condition monitoring unit. The system additionally includes a circuit for detecting a geographic position, wherein the condition monitoring unit is configured to be at least one of activated and deactivated depending on the detected geographic position.

A system includes a mobile platform that moves under remote and/or autonomous control, a sensor package supported by the mobile platform that obtains information relating to a component of a transportation network, and one or more processors that receive the sensor information and analyze the information in combination with other information that is not obtained from the sensor package. The processors also generate an output that displays information relating to one or more of a status, a condition, and/or a state of health of the component of the transportation network; initiates an action to change an operational state of the component; identifies a hazard to one or more vehicles traveling within the transportation network; and/or collects the information relating to the component. Optionally, the component is not communicatively coupled to an information network and the mobile platform provides the information obtained by the sensor package to the information network.

A system and a method for preventing collision between a first train and a second train are provided. The system includes at least one first subsystem installed on a first train. The at least one first subsystem is configured to broadcast a message indicative of a status of the first train. The system further includes at least one second subsystem installed on a second train configured to selectively receive the broadcast message from the at least one first subsystem on the first train. The second subsystem determines the status of the first train by analyzing the broadcast message. An action to be performed at the second train for preventing a collision between the first train and the second train is determined based on the status of the first train. One or more instructions are generated for performing the action at the second train.

Methods and apparatus for real time machine vision and point-cloud data analysis are provided, for remote sensing and vehicle control. Point cloud data can be analyzed via scalable, centralized, cloud computing systems for extraction of asset information and generation of semantic maps. Machine learning components can optimize data analysis mechanisms to improve asset and feature extraction from sensor data. Optimized data analysis mechanisms can be downloaded to vehicles for use in on-board systems analyzing vehicle sensor data. Semantic map data can be used locally in vehicles, along with onboard sensors, to derive precise vehicle localization and provide input to vehicle to control systems.

A method and system include a vehicle system including a plurality of vehicles. The vehicle system is configured to travel along a route. A plurality of location determination devices are onboard the vehicle system. Each of the plurality of location determination devices is configured to output position data regarding a location of at least one of the plurality of vehicles. A handling unit is in communication with the plurality of location determination devices. The handling unit is configured to receive the position data from the plurality of location determination devices and control separation distances between the plurality of vehicles based on the position data.

A event recorder system carried on-board a locomotive may include a controller configured to receive data from one or more of a train management system, a cab signaling system, an energy management system, an exhaust aftertreatment monitoring system, a braking system, a communications management system, and an operator fatigue or distraction monitoring system. The data may include one or more of video data, audio data, sensor output data, GPS map data, scanned image data of an operator in a cab of the locomotive, and operational data associated with operation of the locomotive during a trip. The controller may be configured to synchronize the data based on a common attribute associated with all of the data received by the controller, receive from a user one or more customizable parameters predetermined to be indicative of one or more of a system-related characteristic or behavior associated with any one or more of the systems from which the controller receives data, and generate a system notification based on an indication of one or more of a system-related characteristic or behavior that one of falls outside of a predetermined range of system-determinable parameters, or indicates a trend in one or more system-determinable parameters that deviates from an expected trend in the one or more system-determinable parameters.