A control system and a method for vehicle control in geographical control zones is provided. The control system receives traffic information, including a plurality of image frames of a group of moving objects in a geographical control zone and generates a set of images frames of a first moving object of the group of moving objects based on application of a trained Neural Network (NN) model on the received traffic information. The generated set of image frames corresponds to a set of likely positions of the first moving object at a future time instant. The control system predicts the unsafe behavior of the first moving object based on the generated set of image frames and generates first control information, including an alternate route for a first vehicle in the geographical control zone based on the predicted unsafe behavior. The first vehicle is controlled based on the generated first control information.

A control system and a method for vehicle control in geographical control zones is provided. The control system receives traffic information, including a plurality of image frames of a group of moving objects in a geographical control zone and generates a set of images frames of a first moving object of the group of moving objects based on application of a trained Neural Network (NN) model on the received traffic information. The generated set of image frames corresponds to a set of likely positions of the first moving object at a future time instant. The control system predicts the unsafe behavior of the first moving object based on the generated set of image frames and generates first control information, including an alternate route for a first vehicle in the geographical control zone based on the predicted unsafe behavior. The first vehicle is controlled based on the generated first control information.

A vehicle treatment system includes a signalling device with at least one sensor and a signal generator. The sensor can continuously detect the position of the vehicle in a predetermined approach region in front of or in the vehicle treatment system. The signal generator can output a signal which changes continuously or discretely and corresponds to a continuously changing position of the vehicle. In this case, the signalling device has at least two lights arranged above one another. A corresponding method can be used for determining and displaying entry information.

A vehicle treatment system includes a signalling device with at least one sensor and a signal generator. The sensor can continuously detect the position of the vehicle in a predetermined approach region in front of or in the vehicle treatment system. The signal generator can output a signal which changes continuously or discretely and corresponds to a continuously changing position of the vehicle. In this case, the signalling device has at least two lights arranged above one another. A corresponding method can be used for determining and displaying entry information.

A traffic monitoring apparatus includes: at least one memory storing instructions; and at least one processor. The processor is configured to execute the instructions to; acquire waterfall data from a distributed acoustic sensor (DAS), wherein the waterfall data includes a generation position of a vibration on a roadway adjacent to the DAS, a generation time of the vibration and an amplitude of the vibration; preprocess the waterfall data; estimate at least one enhancement of the processed waterfall data, wherein an enhancement corresponds to a traffic flow property; and estimate at least one traffic flow property of the roadway from the enhancements of the processed waterfall data.

A traffic monitoring apparatus includes: at least one memory storing instructions; and at least one processor. The processor is configured to execute the instructions to; acquire waterfall data from a distributed acoustic sensor (DAS), wherein the waterfall data includes a generation position of a vibration on a roadway adjacent to the DAS, a generation time of the vibration and an amplitude of the vibration; preprocess the waterfall data; estimate at least one enhancement of the processed waterfall data, wherein an enhancement corresponds to a traffic flow property; and estimate at least one traffic flow property of the roadway from the enhancements of the processed waterfall data.

System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

System, methods, and other embodiments described herein relate to improving monitoring of traffic flows. In one embodiment, a method includes aggregating perception data associated with a road network from information sources to a server over a network. The method also includes generating a graph structure from the perception data in association with a neural network model. The graph structure is an incomplete representation of the road network in view of missing data. The method also includes completing the graph structure using the neural network model that forms a graph model of the traffic flows to de-noise the graph structure according to road constraints between two points in the road network. The method also includes communicating the graph model of the traffic flows to a vehicle to navigate traffic in the road network.

A system and a method for automatically tracking location of a vehicle by receiving a plurality of feature vectors transmitted from a plurality of stationary sensors installed along a road. The feature vectors are related to a plurality of vehicles which are in a range of detection of the plurality of the stationary sensors. The method may include clustering a group of feature vectors from the plurality of feature vectors to a monitored vehicle based on a predetermined driving model, determining parameters of the monitored vehicle, calculating a next expected location of the monitored vehicle, receiving a feature vector, matching the feature vector to the monitored vehicle and tracking a trajectory of the monitored vehicle.

A system and a method for automatically tracking location of a vehicle by receiving a plurality of feature vectors transmitted from a plurality of stationary sensors installed along a road. The feature vectors are related to a plurality of vehicles which are in a range of detection of the plurality of the stationary sensors. The method may include clustering a group of feature vectors from the plurality of feature vectors to a monitored vehicle based on a predetermined driving model, determining parameters of the monitored vehicle, calculating a next expected location of the monitored vehicle, receiving a feature vector, matching the feature vector to the monitored vehicle and tracking a trajectory of the monitored vehicle.