Conditional transition maps: Learning motion patterns in dynamic environments

In this paper we introduce a method for learning motion patterns in dynamic environments. Representations of dynamic environments have recently received an increasing amount of attention in the research community. Understanding dynamic environments is seen as one of the key challenges in order to enable autonomous navigation in real-world scenarios. However, representing the temporal dimension is a challenge yet to be solved. In this paper we introduce a spatial representation, which encapsulates the statistical dynamic behavior observed in the environment. The proposed Conditional Transition Map (CTMap) is a grid-based representation that associates a probability distribution for an object exiting the cell, given its entry direction. The transition parameters are learned from a temporal signal of occupancy on cells by using a local-neighborhood cross-correlation method. In this paper, we introduce the CTMap, the learning approach and present a proof-of-concept method for estimating future paths of dynamic objects, called Conditional Probability Propagation Tree (CPPTree). The evaluation is done using a real-world dataset collected at a busy roundabout.