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This paper presents an occupancy grid tracking system based on particles, and the use of this system for dynamic obstacle detection in driving environments. The particles will have a dual nature they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell depending on their motion model and motion parameters, but they will also be created and destroyed using a weighting-resampling mechanism specific to particle filter algorithms. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The dynamic occupancy grid is used for improving the quality of the stereovision-based reconstruction as oriented cuboids. The resulted system is a flexible, real-time tracking solution for dynamic unstructured driving environments, and a useful tool for extracting intermediate dynamic information that can considerably improve object detection and tracking.