🚗 Trajectory Prediction

A repository of trajectory prediction models (PyTorch Base).

💡 Introduction

The goal of trajectory prediction is to predict future positions given their previous positions. These trajectories can be pedestrians or cars. This is a challenging task that is gaining increasing attention in recent years because its applications are becoming more and more relevant. These applications include human surveillance, socio-robot navigation and autonomous driving.

This is my first try on this research direction. I choose to implement some models to extend my knowledge about this. For now, this repo has:

Dataset:

TrajNet++ CFF6 dataset init and visualization; Dataset Link
VGRNN [1] Facebook dataset init and visualization. Dataset Link

Model:

VGRNN (modified from Giggle2526)
~~EvolveGraph~~

🔧 Environment

Python Version: Tested in Python 3.9
Python Packages: torch, torch_geometric, networkx, etc

📁 Structure

.
├── config
├── data/
│   ├── cff
│   └── fb
├── fig
├── notebook
└── src

config: model training parameters
data/cff: TrajNet++ CFF6 dataset, cff_06.ndjson 150MB (you download by yourself)
data/fb: VGRNN Facebook dataset (already included)
notebook: some examples of dataset visualization and model training are here

📖 Guide

Step 1. [Download] or [Clone] this repository;

Step 2. Install require packages: pip insall -r requirement.txt;

Step 3. Check notebook/00_datasete.ipynb to have ideas about two datasets;

Step 4. Check notebook/01_vgrnn.ipynb to see the useage of VGRNN model.

📊 Examples

VGRNN Dataset Visualization

This figure is referred from the original repository. I think this is cooler.

TrajNet++ CFF6 Dataset Visualization

I randomly choose four scenes to show: 150, 170, 190, 210.

VGRNN Training Accuracy

For hyperparaeters, please refer to confg/vgrnn_args.py.

📜 Reference

[1] Hajiramezanali, Ehsan, et al. "Variational graph recurrent neural networks." Advances in neural information processing systems 32 (2019).

cbhua/trajectory-prediction