G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 15 Oct 2022.
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12 of 20 references.
References · 12
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Citing papers per year: 2026: 1.
1 paper.