DPGP for Multi-Vehicle Interaction Scenario Extraction

This repo provides the python implementation of DPGP algorithm using Gaussian Process to represent multi-vehicle driving scenarios with Dirichlet Process adapting cluster numbers.
The back-bone DPGP python package is forked from https://github.com/mxu34/DPGP. The authors would like to thank Mengdi Xu for her great support.
Initial MATLAB code implemented by Yaohui Guo, Vinay Varma Kalidindi.

Paper Reference:

Modeling Multi-Vehicle Interaction Scenarios Using Gaussian Random Field
https://arxiv.org/pdf/1906.10307.pdf

Improvement:

The code structure is more clear and can easily be implemented for various applications.
Thanks members of SafeAI lab for discussion!

Input:

frames: list with element as object defined in frame.py

Output:

Mixture model as defined in mixtureModel.py

Implement:

Train DPGP: python main.py
Visualization: python pattern_vis.py

Required python packages:

numpy == 1.16.4
scipy == 1.3.1
scikit-learn == 0.21.2
pandas == 0.25.0
Some others:
math
multiprocessing
functools
pickle