https://github.com/stanfordasl/trajectron
Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs" by Boris Ivanovic and Marco Pavone.
https://github.com/stanfordasl/trajectron
deep-learning human-robot-interaction human-trajectory-prediction
Last synced: about 1 year ago
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Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs" by Boris Ivanovic and Marco Pavone.
- Host: GitHub
- URL: https://github.com/stanfordasl/trajectron
- Owner: StanfordASL
- License: mit
- Created: 2018-09-15T18:17:48.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-12-09T20:12:29.000Z (over 5 years ago)
- Last Synced: 2023-12-19T13:20:41.580Z (over 2 years ago)
- Topics: deep-learning, human-robot-interaction, human-trajectory-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 130 MB
- Stars: 121
- Watchers: 10
- Forks: 40
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**NOTE:** A new version of the Trajectron has been released! Check out [Trajectron++](https://github.com/StanfordASL/Trajectron-plus-plus)!
# The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs
This repository contains the code for [The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs](https://arxiv.org/abs/1810.05993) by Boris Ivanovic and Marco Pavone.
## Installation ##
First, we'll create a conda environment to hold the dependencies.
```
conda create --name dynstg python=3.6 -y
source activate dynstg
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 dynstg --display-name "Python 3.6 (DynSTG)"
```
Now, you can start a Jupyter session and view/run all the 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.
* `code/test_online.py` - Replays a scene from a dataset and performs online inference with a trained Trajectron.
* `code/evaluate_alongside_sgan.py` - Evaluates the performance of the Trajectron against Social GAN. This script mainly collects evaluation data, which can be visualized with `sgan-dataset/Result Analyses.ipynb`.
* `code/compare_runtimes.py` - Evaluates the runtime of the Trajectron against Social GAN. This script mainly collects runtime data, which can be visualized with `sgan-dataset/Runtime Analysis.ipynb`.
* `sgan-dataset/Qualitative Plots.ipynb` - Can be used to visualize predictions from the Trajectron alone, or against those from Social GAN.
## Datasets ##
The preprocessed datasets are available in this repository, under `data/` folders (i.e. `sgan-dataset/data/`).
If you want the *original* ETH or UCY datasets, you can find them here: [ETH Dataset](http://www.vision.ee.ethz.ch/en/datasets/) and [UCY Dataset](https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data).