Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals

This repository contains code for "Multimodal trajectory prediction conditioned on lane-graph traversals" by Nachiket Deo, Eric M. Wolff and Oscar Beijbom, presented at CoRL 2021.

@inproceedings{deo2021multimodal,
  title={Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals},
  author={Deo, Nachiket and Wolff, Eric and Beijbom, Oscar},
  booktitle={5th Annual Conference on Robot Learning},
  year={2021}
}

Note: While I'm one of the authors of the paper, this is an independent re-implementation of the original code developed during an internship at Motional. The code follows the implementation details in the paper. Hope this helps! -Nachiket

Installation

1. Clone this repository

2. Set up a new conda environment

conda create --name pgp python=3.7

3. Install dependencies

conda activate pgp

# nuScenes devkit
pip install nuscenes-devkit

# Pytorch: The code has been tested with Pytorch 1.7.1, CUDA 10.1, but should work with newer versions
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch

# Additional utilities
pip install ray
pip install psutil
pip install positional-encodings==5.0.0
pip install imageio
pip install tensorboard

Dataset

1. Download the nuScenes dataset. For this project we just need the following.

   • Metadata for the Trainval split (v1.0)
   • Map expansion pack (v1.3)

2. Organize the nuScenes root directory as follows

└── nuScenes/
    ├── maps/
    |   ├── basemaps/
    |   ├── expansion/
    |   ├── prediction/
    |   ├── 36092f0b03a857c6a3403e25b4b7aab3.png
    |   ├── 37819e65e09e5547b8a3ceaefba56bb2.png
    |   ├── 53992ee3023e5494b90c316c183be829.png
    |   └── 93406b464a165eaba6d9de76ca09f5da.png
    └── v1.0-trainval
        ├── attribute.json
        ├── calibrated_sensor.json
        ...
        └── visibility.json         

3. Run the following script to extract pre-processed data. This speeds up training significantly.

python preprocess.py -c configs/preprocess_nuscenes.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data

Inference

You can download the trained model weights using this link. Since the original link is not working any more, you can find the weights in the directory, namely the PGP_lr-scheduler.tar file.

To evaluate on the nuScenes val set run the following script. This will generate a text file with evaluation metrics at the specified output directory. The results should match the benchmark entry on Eval.ai.

python evaluate.py -c configs/pgp_gatx2_lvm_traversal.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -w path/to/trained/weights

To visualize predictions run the following script. This will generate gifs for a set of instance tokens (track ids) from nuScenes val at the specified output directory.

python visualize.py -c configs/pgp_gatx2_lvm_traversal.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -w path/to/trained/weights

Training

To train the model from scratch, run

python train.py -c configs/pgp_gatx2_lvm_traversal.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -n 100

The training script will save training checkpoints and tensorboard logs in the output directory.

To launch tensorboard, run

tensorboard --logdir=path/to/output/directory/tensorboard_logs