  This work addresses on the following problem: given a set of unsynchronized
history observations of two scenes that are correlative on their dynamic
changes, the purpose is to learn a cross-scene predictor, so that with the
observation of one scene, a robot can onlinely predict the dynamic state of
another. A method is proposed to solve the problem via modeling dynamic
correlation using latent space shared auto-encoders. Assuming that the inherent
correlation of scene dynamics can be represented by shared latent space, where
a common latent state is reached if the observations of both scenes are at an
approximate time, a learning model is developed by connecting two auto-encoders
through the latent space, and a prediction model is built by concatenating the
encoder of the input scene with the decoder of the target one. Simulation
datasets are generated imitating the dynamic flows at two adjacent gates of a
campus, where the dynamic changes are triggered by a common working and
teaching schedule. Similar scenarios can also be found at successive
intersections on a single road, gates of a subway station, etc. Accuracy of
cross-scene prediction is examined at various conditions of scene correlation
and pairwise observations. Potentials of the proposed method are demonstrated
by comparing with conventional end-to-end methods and linear predictions.
