论文标题
通过使用潜在空间共享自动编码器建模动态相关性的跨场景预测
Cross Scene Prediction via Modeling Dynamic Correlation using Latent Space Shared Auto-Encoders
论文作者
论文摘要
这项工作涉及以下问题：给定两种与它们的动态变化相关的不同步历史记录观察，目的是学习一个跨景点预测指标，以便通过对一个场景的观察，一个机器人可以在某个场景上进行观察，从而可以在另一个场景中预测另一个场景的动态状态。提出了一种方法来通过使用共享的自动编码器对动态相关进行建模来解决问题。假设场景动态的固有相关性可以通过共享潜在空间来表示，如果两个场景的观察在大概的时间都在大约时间内达到共同的潜在状态，则通过通过潜在空间连接两个自动编码器来开发学习模型，并且通过将预测模型连接到与一个目标的一个目标相结合来构建的预测模型。生成模拟数据集，模仿校园的两个相邻门的动态流，在该大门中，动态变化是由共同的工作和教学时间表触发的。在单个道路，地铁站的大门等连续的交叉点上也可以找到类似的情况。在场景相关和成对观测的各种条件下，检查了跨景点预测的准确性。通过与常规的端到端方法和线性预测进行比较来证明所提出方法的电位。
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.
