Open AccessGenerative Pedestrian Trajectory Prediction with Graph Representation
Author(s) -
Haoyu Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012149
Subject(s) - computer science , measure (data warehouse) , usability , generative model , trajectory , artificial intelligence , gaussian , robot , machine learning , graph , pedestrian , data mining , representation (politics) , displacement (psychology) , generative grammar , theoretical computer science , engineering , human–computer interaction , psychology , quantum mechanics , psychotherapist , physics , astronomy , politics , transport engineering , law , political science
Multi-agent trajectory prediction is one of the core modules of unmanned driving and intelligent robots. The traditional method is difficult to measure the relationship between multiple agents, and the modeling ability is rigid. Nowadays, most of the methods make less use of geographic information, and social relationship modeling is not sufficient. Our model uses Graph Neural Network (GNN) to measure social relationships to improve its usability. The model is built by the basic Conditional Auto-encoder (CVAE) framework, using Gaussian Mixture Model to mix multiple Gaussian distributions and the possibility of obtaining more potential space through dynamic integration to make the model achieve better results. Our model has achieved excellent results on the Stanford Drone Dataset. The evaluation by average displacement error (ADE) and final displacement error (FDE) metrics has exceeded the majority of existing models.