Abstract: 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.

Abstract: A system and method for determining a viewpoint of a traffic camera includes obtaining images of a real road captured by the traffic camera, segmenting a road surface from the captured images to generate a mask of the real road, generating a 3D model of a simulated road corresponding to the real road, from geographical data of the real road, adding a simulated camera corresponding to the traffic camera to a location in the 3D model that is corresponding to a location of the traffic camera in the real road, generating a plurality of simulated images of the simulated road using the 3D model, each corresponding to a set of viewpoint parameters of the simulated traffic camera, selecting the simulated image that provides the best fit between the simulated image and the mask, and generating mapping between pixel locations in the captured images and locations on the real road.

Abstract: A system and a method for automatically identifying and classifying traffic and road events, by collecting metrics regarding one or more monitored vehicles from at least one of: a plurality of stationary traffic sensors installed on or proximate to a discrete segment of a road; traffic monitoring infrastructure; and, one or more connected vehicles. The system and a method are further to determine a position and a speed of each monitored vehicle per discrete segment in each lane of said road; and, identify, classify and localize one or more traffic and road events on said road. The identifying and classifying includes the steps of: training one or more machine learning algorithms to derive traffic and road events; and, associating, using said one or more machine learning algorithms, one or more observed traffic and road events with a known class of traffic and road events.

Abstract: A method for directing people in a space which is represented by a spatial network is disclosed. The spatial network is color-coded and movement is restricted to the space which this network represents. The method allows people to find paths and move in the space by matching a sequence of colors to the colors of graphical elements which they see in the space. Also disclosed are various methods and devices for a wayfinding system that helps people to orient themselves and navigate to various locations in a space. Finally disclosed is a game that allows random movement of playing tokens in a playing space where movement is restricted to a color-coded spatial network.