G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System

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 or data augmentations using hidden markov modeling and reinforcement learning based agents. Additionally, we propose a simple geometry-inspired loss and evaluation metric for trajectory non-linearity analysis. Code available at [Anonymous-repository](https://github.com/ANonyMouxe/GPECNet)