Provided are systems, methods, and computer readable media for predicting a vessel rendezvous, and systems, methods, and computer readable media for generating a vessel rendezvous prediction model. The method can include generating or receiving a rendezvous a rendezvous prediction model; receiving vessel data for a plurality of vessels from one or more sources; constructing a vessel trajectory for each vessel of the plurality of vessels based on the vessel data, each vessel trajectory comprising one or more trajectory segments; providing the plurality of constructed vessel trajectories to the rendezvous prediction model; and generating, at the processor, a rendezvous prediction output from the rendezvous prediction model.

A method of detecting a risk of collision between a boat and an object in a water area using a camera module mounted on said boat, said camera module having a RGB camera, said boat being characterized by its course, said method having the steps of generating at least one sequence of images using said camera, detecting at least one light source in the images of the at least one sequence of images, said at least one light source being mounted on a luminous object located in the water area, calculating a series of polar angles between the boat and the at least one light source using the images of the at least one sequence of images, estimating the course direction of the object with regard to the boat by deriving said series of calculated polar angles with respect to time, detecting a risk of collision between the boat and the object when the estimated course direction of the object leads said object towards the course of the boat.

A method of detecting a risk of collision between a boat and an object in a water area using a camera module mounted on said boat, said camera module having a RGB camera, said boat being characterized by its course, said method having the steps of generating at least one sequence of images using said camera, detecting at least one light source in the images of the at least one sequence of images, said at least one light source being mounted on a luminous object located in the water area, calculating a series of polar angles between the boat and the at least one light source using the images of the at least one sequence of images, estimating the course direction of the object with regard to the boat by deriving said series of calculated polar angles with respect to time, detecting a risk of collision between the boat and the object when the estimated course direction of the object leads said object towards the course of the boat.

A method of operation of a vehicle system including capturing a current image from a current location towards a travel direction along a travel path; generating an image category for the current image based on a weather condition, the current location, or a combination thereof; determining a clear path towards the travel direction of the travel path based on the image category, the current image, and a previous image; and communicating the clear path for assisting in operation of a vehicle.

The present invention relates to a method and a system for recognition of objects near a ship by using a deep neural network to prevent a collision with the object by recognizing a neighboring object that may be risky to the ship sailing in a restricted condition such as a foggy environment. All object movements within a predetermined radius are detected and recognized so that collision accidents with objects on the sea in an environment such as fog caused by bad weather at sea can be prevented, and a risk alarm is notified to a captain when the object is detected so that collision accidents can be remarkably reduced. In addition, peripheral environments are detected by only installing a CCTV camera so that expenses can be reduced, human negligence can be prevented, and the system can be easily constructed to prevent collisions.

The present invention relates to a method and a system for recognition of objects near a ship by using a deep neural network to prevent a collision with the object by recognizing a neighboring object that may be risky to the ship sailing in a restricted condition such as a foggy environment. All object movements within a predetermined radius are detected and recognized so that collision accidents with objects on the sea in an environment such as fog caused by bad weather at sea can be prevented, and a risk alarm is notified to a captain when the object is detected so that collision accidents can be remarkably reduced. In addition, peripheral environments are detected by only installing a CCTV camera so that expenses can be reduced, human negligence can be prevented, and the system can be easily constructed to prevent collisions.

A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

A method for situation awareness is provided. The method comprises: preparing a neural network trained by a learning set, wherein the learning set includes a plurality of maritime images and maritime information including object type information which includes a first type index for a vessel, a second type index for a water surface and a third type index for a ground surface, and distance level information which includes a first level index indicating that a distance is undefined, a second level index indicating a first distance range and a third level index indicating a second distance range greater than the first distance range; obtaining a target maritime image generated from a camera; and determining a distance of a target vessel based on the distance level index of the maritime information being outputted from the neural network which receives the target maritime image and having the first type index.

The present disclosure relates to a method for determining an action for collision avoidance in a craft. The method (100) comprises obtaining (110) object data comprising three-dimensional object data points (420); obtaining (120) state data of the craft (260); determining (140) at least one set of manoeuvre paths (410a,b,c) for the craft (260) based on the obtained craft state data; determining (150) a set of distance thresholds (421) for the three-dimensional object data points (420) based on the object data; comparing (160) each set of manoeuvre paths (410a,b,c) with the object data and the set of distance thresholds (421), wherein the set of manoeuvre paths (410a,b,c) is identified as a colliding set of manoeuvre paths (410a,b,c) when each path of the set of manoeuvre paths (410a,b,c) is at least partially within the corresponding distance threshold (421) of at least one three-dimensional object data point (420); and determining (170) an action upon identification of at least one colliding set of manoeuvre paths (410a,b,c).

The present disclosure relates to a method for determining an action for collision avoidance in a craft. The method (100) comprises obtaining (110) object data comprising three-dimensional object data points (420); obtaining (120) state data of the craft (260); determining (140) at least one set of manoeuvre paths (410a,b,c) for the craft (260) based on the obtained craft state data; determining (150) a set of distance thresholds (421) for the three-dimensional object data points (420) based on the object data; comparing (160) each set of manoeuvre paths (410a,b,c) with the object data and the set of distance thresholds (421), wherein the set of manoeuvre paths (410a,b,c) is identified as a colliding set of manoeuvre paths (410a,b,c) when each path of the set of manoeuvre paths (410a,b,c) is at least partially within the corresponding distance threshold (421) of at least one three-dimensional object data point (420); and determining (170) an action upon identification of at least one colliding set of manoeuvre paths (410a,b,c).