  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
