CommonRoad / crgeo

Graph neural networks for autonomous driving
https://commonroad.in.tum.de/tools/commonroad-geometric
BSD 3-Clause "New" or "Revised" License
33 stars 3 forks source link
autonomous-vehicles deep-learning graph-neural-networks pytorch

Introduction

commonroad-geometric (crgeo) is a Python framework that facilitates deep-learning based research projects in the autonomous driving domain, e.g. related to behavior planning and state representation learning.

At its core, it provides a standardized interface for heterogeneous graph representations of traffic scenes using the PyTorch-Geometric framework.

The package aims to serve as a flexible framework that, without putting restrictions on potential research directions, minimizes the time spent on implementing boilerplate code. Through its object-oriented design with highly flexible and extendable class interfaces, it is meant to be imported via pip install and utilized in a plug-and-play manner.

Highlighted features


Getting started

The easiest way of getting familiar with the framework is to consult the tutorial directory, which contains a multitude of simple application demos that showcase the intended usage of the package.

Research guidelines:


Installation

The installation script scripts/create-dev-environment.sh installs the commonroad-geometric package and all its dependencies into a conda environment:

Execute the script inside the directory which you want to use for your development environment.

Note: make sure that the CUDA versions are compatible with your setup.

Note: Headless rendering

If you want to export the rendering frames without the animation window popping up, please use the command given below.

echo "export PYGLET_HEADLESS=..." >> ~/.bashrc

You can replace .bashrc with .zshrc, if you use zsh