Aspects of the present disclosure describe distributed fiber optic sensing (DFOS) systems, methods, and structures that advantageously enable and/or facilitate the continuous, real-time monitoring and identification vehicle speed, vehicle direction, vehicle axle width, vehicle type, total number of vehicle axles, and vehicle count. The DFOS sensing fiber is advantageously positioned underneath a roadway/highway in a novel arrangement/layout and temporal measurements are made to provide vehicle identification.

In order, when tracking moving objects, to be able to track the path of the objects without using object-identifying information, e.g. GPS information of the object or other individual identifiers by means which an object can be basically identified, or if the objects continue to change their respective identity, it is proposed how a statement can be made about the successful tracking with respect to at least some of the object group along the tracking route on the basis of the acquisition of information, taking place along a tracking route repeatedly, particularly at different locations at different times, with non-object-identifying parameter data which is emitted by the moving objects, at regular time intervals, by object-group-specific considerations of the acquired parameter data, of group-specific data profiles, acquired from these considerations, and a data profile similarity comparison, and tracking information is generated, and otherwise no such statement is possible and the generation of the tracking information does not occur.

Methods, systems, computer-readable media, and apparatuses for automated dynamic lane definition are presented. One example method includes the steps of accessing vehicle information for a vehicle travelling on the road; accessing road information for a road; and defining at least one lane for the road based on the road information and the vehicle information.

Aspects of the present disclosure relate to a vehicle for maneuvering an occupant of the vehicle to a destination autonomously as well as providing information about the vehicle and the vehicle's environment for display to the occupant.

Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

Embodiments relate to a system, computer program product, and method for determining shelter areas for two-wheeler vehicles, and, more specifically, for dynamically distinguishing the behavior of two-wheeler vehicles and non-two-wheeler vehicles as an indicator of shelter areas from inclement weather. The behavior of the vehicles is distinguished through a plurality of two-wheeler vehicles slowing down and congregating at a particular location as a shelter against inclement weather, while non-two-wheeler vehicles may slow down, however, not stop proximate this location.

Lanes sensors are used to count the number of wheel assemblies on vehicles passing over roadway sensors. Lane sensors can also be used to classify vehicles at single and multiple lane sites for tolling and/or traffic planning. For counting vehicles, the Smart Loop Treadle of the present invention is designed for both tire and wheel assembly detection using inductive loop sensors for toll roads in single (Conventional) lane applications. The sensors detect the tire assemblies of both vehicles and vehicle trailers being towed to provide the sum of axle assemblies. For vehicle characterization, the sensor arrangement can have a combination of unique sensors that include tire/wheel detection sensors and vehicle lane position sensors. The characteristics of the vehicle, travel direction, speed, in lane position of the vehicle can be detected using combination of these sensors.

Systems and methods for tracking objects though a traffic control system include an image sensor configured to capture a stream of images of scene from an associated real-world position, an object tracker configured to identify an object in the captured images and define an associated object location in the captured images, a three-dimensional stage model system configured to transform the associated object location in the image to three-dimensional coordinates associated with the image sensor, and a three-dimensional world model configured to transform identified objects to real-world coordinates. Embodiments use lens aberration, sensor mounting height and location, accelerometer, gyro-compass and/or global position satellite information to generate a situational map.

Provided an apparatus including: a signal acquisition part that acquires an oscillation signal from a sensor that detects an oscillation induced in a target object; and an estimation part that obtains a feature value for each frame of the oscillation signal by applying Fourier transform to each frame extracted by a window of a predetermined length to calculate the feature value for the each frame in a frequency domain, and performs Gaussian mixture model-clustering on a time series of the feature values for respective frames to estimate one or more clusters, each of which is modeled with a Gaussian probability distribution best fit to the time series, and detect one or more events by detecting one or more corresponding clusters, a probability density value thereof greater than a predetermined threshold value.

Provided an apparatus including: a signal acquisition part that acquires an oscillation signal from a sensor that detects an oscillation induced in a target object; and an estimation part that obtains a feature value for each frame of the oscillation signal by applying Fourier transform to each frame extracted by a window of a predetermined length to calculate the feature value for the each frame in a frequency domain, and performs Gaussian mixture model-clustering on a time series of the feature values for respective frames to estimate one or more clusters, each of which is modeled with a Gaussian probability distribution best fit to the time series, and detect one or more events by detecting one or more corresponding clusters, a probability density value thereof greater than a predetermined threshold value.