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.

中文翻译：

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通过使用潜在空间共享自动编码器建模动态相关性进行跨场景预测

该工作解决以下问题：给定一组与其动态变化相关的两个场景的非同步历史观察，目的是学习跨场景预测器，以便通过观察一个场景，机器人可以在线预测另一个的动态状态。提出了一种通过使用潜在空间共享自动编码器对动态相关性进行建模来解决该问题的方法。假设场景动态的内在相关性可以由共享的潜在空间表示，如果两个场景的观察时间接近，则达到共同的潜在状态，通过潜在空间连接两个自动编码器来开发学习模型，并通过将输入场景的编码器与目标场景的解码器连接起来构建预测模型。模拟数据集是模拟校园两个相邻大门处的动态流动而生成的，其中动态变化是由共同的工作和教学计划触发的。在单条道路上的连续交叉路口、地铁站的大门等处也可以找到类似的场景。在场景相关性和成对观察的各种条件下检查跨场景预测的准确性。通过与传统的端到端方法和线性预测进行比较，证明了所提出方法的潜力。在场景相关和成对观察的各种条件下检查跨场景预测的准确性。通过与传统的端到端方法和线性预测进行比较，证明了所提出方法的潜力。在场景相关和成对观察的各种条件下检查跨场景预测的准确性。通过与传统的端到端方法和线性预测进行比较，证明了所提出方法的潜力。