论文标题
G-Pecnet：朝向可推广的行人轨迹预测系统
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
论文作者
论文摘要
在不阻碍或损害人类资产的情况下导航动态的物理环境对于社会机器人至关重要。在这项工作中，我们解决了使用深层生成模型来预测人类和试剂轨迹的自主无人机导航的子问题。我们的方法：General-Pecnet或G-Pecnet在2020年的最终位移误差（FDE）上观察到9.5 \％的改善：通过周期性激活功能和合成模型（数据）增强使用隐藏的Markov模型（HMMS）和强化学习（pecnections（数据）增强），通过建筑改进的结合（PECNET）。此外，我们为轨迹非线性和异常检测提出了一个简单的几何启发度量，对任务有帮助。可在https://github.com/aryan-garg/pecnet-pedestrian-trajectory-prediction.git上获得代码
Navigating dynamic physical environments without obstructing or damaging human assets is of quintessential importance for social robots. In this work, we solve autonomous drone navigation's sub-problem of predicting out-of-domain human and agent trajectories using a deep generative model. Our method: General-PECNet or G-PECNet observes an improvement of 9.5\% on the Final Displacement Error (FDE) on 2020's benchmark: PECNet through a combination of architectural improvements inspired by periodic activation functions and synthetic trajectory (data) augmentations using Hidden Markov Models (HMMs) and Reinforcement Learning (RL). Additionally, we propose a simple geometry-inspired metric for trajectory non-linearity and outlier detection, helpful for the task. Code available at https://github.com/Aryan-Garg/PECNet-Pedestrian-Trajectory-Prediction.git
