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.
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
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
When you're done, don't forget to deactivate the conda environment with
Run any of these with a
--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
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/datafor more information).
code/notebooks/NuScenes Qualitative.ipynb- Visualizes the predictions that the Trajectron++ makes.
A sample of fully-processed scenes from the nuScenes dataset are available in this repository, in
If you want the original nuScenes dataset, you can find it here: nuScenes Dataset.