Methods, devices, and systems for vehicle tracking are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to receive tracking data for an vehicle at an airport, where the tracking data includes a data record including a number of data fields, determine whether the vehicle is being actively tracked using the tracking data, and generate a mapped data record using the tracking data to track the vehicle at the airport in response to the vehicle being actively tracked.

Tracking aircraft in and near a ramp area is described herein. One method includes receiving camera image data of an aircraft while the aircraft is approaching or in the ramp area, receiving LIDAR/Radar sensor data of an aircraft while the aircraft is approaching or in the ramp area, merging the camera image data and the LIDAR/Radar sensor data into a merged data set, and wherein the merged data set includes at least one of: data for determining the position and orientation of the aircraft relative to the position and orientation of the ramp area, data for determining speed of the aircraft, data for determining direction of the aircraft, data for determining proximity of the aircraft to a particular object within the ramp area, and data for forming a three dimensional virtual model of at least a portion of the aircraft from the merged data.

A sensor assembly for use in association with non-integrated, ground-based collision avoidance systems for aircraft, including (a) a sensor; and (b) a frame sub-assembly, wherein the sensor is releasably securable to the frame sub-assembly.

A system for audibly communicating a status of at least one operably connected device or system, includes a status module configured to receive a signal representative of a status of the at least one operably connected device or system and a transceiver operably connected to the status module, wherein the transceiver is configured to receive signals from the status module and to transmit a sound.

Systems and methods are disclosed for predicting a hot spot. One method comprises receiving, by a hot spot prediction system, vehicle characteristics associated with a vehicle and traffic data. Then the hot spot prediction system determines a hot spot and an estimated arrival time at the hot spot based on the vehicle characteristics and the traffic data. Following the determination, an auto brake application system receives the hot spot and the estimated arrival time and determines a safe stop time based on the vehicle characteristics, the hot spot and the estimated arrival time. The auto brake application system then sends a notification to the vehicle based on determining that the vehicle can stop within the safe stop time, and performs an action in response to receiving a confirmation from the vehicle.

Tracking aircraft in and near a ramp area is described herein. One method includes receiving camera image data of an aircraft while the aircraft is approaching or in the ramp area, receiving LIDAR/Radar sensor data of an aircraft while the aircraft is approaching or in the ramp area, merging the camera image data and the LIDAR/Radar sensor data into a merged data set, and wherein the merged data set includes at least one of: data for determining the position and orientation of the aircraft relative to the position and orientation of the ramp area, data for determining speed of the aircraft, data for determining direction of the aircraft, data for determining proximity of the aircraft to a particular object within the ramp area, and data for forming a three dimensional virtual model of at least a portion of the aircraft from the merged data.

Anti-collision airport system including a motorized mobile device to be moved close to an aircraft. The system includes a database to record structural characteristics of categories of aircraft and structural characteristics of the motorized mobile device. The controller is configured to identify a category corresponding to the aircraft and a positioning of the motorized mobile device with respect to the aircraft, search and retrieve, from the database, structural characteristics associated with the category corresponding to the aircraft and structural characteristics of the motorized device, calculate a trajectory for the movement of the motorized mobile device on the basis of the structural characteristics of the aircraft and of the motorized mobile device and of the positioning of the motorized mobile device with respect to the aircraft, and control the motorized mobile device according to the calculated trajectory.

Devices, systems, and methods for determining aircraft orientation are described herein. One device includes instructions executable to determine a subsection of a rendering of a portion of an airport, wherein the subsection is associated with a particular stand of the airport, and wherein the subsection includes a plurality of ground travel pathways, determine a first subset of the plurality of pathways, a second subset of the plurality of pathways, and a third subset of the plurality of pathways, track a location of an outbound aircraft within the subsection of the rendering, determine an orientation of the aircraft according to a first set of rules responsive to a determination that the location of the aircraft is within the first or second subset of the plurality of pathways, and determine the orientation of the aircraft according to a second set of rules responsive to a determination that the location of the aircraft is within the third subset of the plurality of pathways.

A ground collision avoidance method in an ownship vehicle is disclosed. The method includes: retrieving position measurements for the ownship vehicle and for a dynamic obstacle; retrieving mapping data from an airport map database that includes coordinate data for airport travel pathways; adjusting a position measurement for the ownship vehicle and a position measurement for the dynamic obstacle based on coordinate data retrieved from the airport map database and historical aircraft movement data; predicting a series of future positions for the ownship vehicle that are constrained by airport surface operation rules; predicting a series of future positions for the dynamic obstacle that are constrained by airport surface operation rules; calculating whether a potential collision is imminent; and causing a collision alert to be displayed when the processor has determined that a potential collision between the ownship vehicle and the dynamic obstacle is imminent.

In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.