  This paper addresses the problem of learning instantaneous occupancy levels
of dynamic environments and predicting future occupancy levels. Due to the
complexity of most real-world environments, such as urban streets or crowded
areas, the efficient and robust incorporation of temporal dependencies into
otherwise static occupancy models remains a challenge. We propose a method to
capture the spatial uncertainty of moving objects and incorporate this
uncertainty information into a continuous occupancy map represented in a rich
high-dimensional feature space. Experiments performed using LIDAR data verified
the real-time performance of the algorithm.
