G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System

• URL: http://arxiv.org/abs/2210.09846v3
• Date: Sun, 31 Mar 2024 07:50:22 GMT
• Title: G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
• Authors: Aryan Garg, Renu M. Rameshan,
• Abstract summary: General-PECNet or G-PECNet observes an improvement of 9.5% on the Final Displacement Error (FDE) on 2020's benchmark. We propose a simple geometry-inspired metric for trajectory non-linearity and outlier detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (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

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