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This work addresses on the following problem: given a set of unsynchronizedhistory observations of two scenes that are correlative on their dynamicchanges, the purpose is to learn a cross-scene predictor, so that with theobservation of one scene, a robot can onlinely predict the dynamic state ofanother. A method is proposed to solve the problem via modeling dynamiccorrelation using latent space shared auto-encoders. Assuming that the inherentcorrelation of scene dynamics can be represented by shared latent space, wherea common latent state is reached if the observations of both scenes are at anapproximate time, a learning model is developed by connecting two auto-encodersthrough the latent space, and a prediction model is built by concatenating theencoder of the input scene with the decoder of the target one. Simulationdatasets are generated imitating the dynamic flows at two adjacent gates of acampus, where the dynamic changes are triggered by a common working andteaching schedule. Similar scenarios can also be found at successiveintersections on a single road, gates of a subway station, etc. Accuracy ofcross-scene prediction is examined at various conditions of scene correlationand pairwise observations. Potentials of the proposed method are demonstratedby comparing with conventional end-to-end methods and linear predictions.

Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders