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Fundamental properties of dynamic occupancy grid systems for vehicle environment perception

Using probabilistic reasoning to model and perceive driving environments is a challenging problem due to both geometric and dynamic random natures. The occupancy grid system, providing an intermediate representation for complicated environments and having many beneficial implications (such as avoiding direct data association and more freedom in data fusion), has been increasingly becoming a popular paradigm for vehicle environment perception. The conventional static occupancy grid (SOG) system only describes static environments; to incorporate dynamic information into the conventional occupancy grid is naturally desirable. The corresponding system is called dynamic occupancy grid (DOG). The DOG systems are still developing and some fundamental questions remain unanswered. In a statistical sense, the DOG extends the SOG in generalizing its grid as a random field defined over a parameter space in not only geometric space but also time domain. In this paper, under a formal statistical definition of the DOG, we carry out a Bayesian analysis and examine commonly-used assumptions and approximations in the literature. In view of works having been done, we present a particle-based multiple model approach for our DOG system and the corresponding results are given in a typical vehicle driving scenario.