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Code accompanying "Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).
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code Initial public release. Jan 13, 2020
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README.md

Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control

This repository contains the code for Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

Specifically, this branch is for the Trajectron++ applied to the nuScenes autonomous driving dataset.

Installation

Note about Submodules

When cloning this branch, make sure you clone the submodules as well, with the following command:

git clone --recurse-submodules <repository cloning URL>

Alternatively, you can clone the repository as normal and then load submodules later with:

git submodule init # Initializing our local configuration file
git submodule update # Fetching all of the data from the submodules at the specified commits

Environment Setup

First, we'll create a conda environment to hold the dependencies.

conda create --name trajectron++ python=3.6 -y
source activate trajectron++
pip install -r requirements.txt

Then, since this project uses IPython notebooks, we'll install this conda environment as a kernel.

python -m ipykernel install --user --name trajectron++ --display-name "Python 3.6 (Trajectron++)"

Now, you can start a Jupyter session and view/run all the notebooks in code/notebooks with

jupyter notebook

When you're done, don't forget to deactivate the conda environment with

source deactivate

Scripts

Run any of these with a -h or --help flag to see all available command arguments.

  • code/train.py - Trains a new Trajectron++ model.
  • code/notebooks/run_eval.bash - Evaluates the performance of the Trajectron++. This script mainly collects evaluation data, which can then be visualized with code/notebooks/NuScenes Quantitative.ipynb.
  • data/nuScenes/process_nuScenes.py - Processes the nuScenes dataset into a format that the Trajectron++ can directly work with, following our internal structures for handling data (see code/data for more information).
  • code/notebooks/NuScenes Qualitative.ipynb - Visualizes the predictions that the Trajectron++ makes.

Datasets

A sample of fully-processed scenes from the nuScenes dataset are available in this repository, in data/processed.

If you want the original nuScenes dataset, you can find it here: nuScenes Dataset.

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