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However, they are in general insufficient for processing high-demand algorithms in parallel with other data. The overall performance of these systems depends on the number of cores, their operating frequencies, memory bandwidth, and size. Currently, common Electronic Control Unit (ECU), based on single or multicore microcontrollers (MCUs) are used for low to medium-speed sensor data processing. The main drawback of this approach is its computational cost, unaffordable for automotive embedded systems. In addition, it produces a compact representation model, as empty space is also conveniently represented for proper situational awareness. Under this paradigm, there is no need for higher level object models, resulting in: (i) a much higher insensitivity to the extreme variability of objects and, (ii) avoidance of the association problem. Occupancy grids overcome these difficulties, and in particular the Bayesian Occupancy Filter (BOF) by projecting objects onto a compact regularly subdivided probabilistic grid, and tracking them as a set of occupied or dynamic cells. In addition, the associated tracking methodology raises the classic problem of object association and state estimation, which are highly coupled. However, urban driving scenarios are so heterogeneous and unpredictable that they are extremely complex to manage under a feature-based perception paradigm. In well structured driving environments, such as highways, the types of static and dynamic objects are easily modeled and tracked using geometrical models and their parameters. One of the most important challenges in those scenarios is the accurate perception of static and moving objects, to properly understand the spatio-temporal relationship between the subject vehicle and the relevant entities. However, the complexity of some highly uncertain and dynamic urban driving scenarios still hampers the deployment of fully automated vehicles. Intelligent vehicle technology is advancing at a vertiginous pace.