G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System

Aryan Garg, Renu Rameshan

Keywords: Pedestrian Prediction, Deep Generative Models, Variational Autoencoder, Data Augmentation, Reinforcement Learning

TL;DR: Deep generative pedestrian trajectory prediction using reinforcement learning based synthetic data, for autonomous navigation.

Abstract: 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)

Submission Number: 14