G-PECNet：汎化可能な歩行者軌道予測システムに向けて
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System
周期活性化関数とHMM・強化学習による合成軌道データ拡張を組み合わせ、歩行者軌道予測モデルPECNetの汎化性能を改善した研究。
著者: Aryan Garg, Renu M. Rameshan
分類: cs.AI, cs.CV, cs.LG, cs.RO
原文アブストラクト
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