Efficient Motion Prediction (EMP)

[Paper]

Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed
Alexander Prutsch, Horst Bischof, Horst Possegger Graz University of Technology
IROS 2024

Abstract

For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.

Getting Started

Create and Activate Virtual Environment

conda create -n emp python=3.8.18
conda activate emp

Install PyTorch

We tested our implementation with torch 1.11.0+cu113 and torch 2.1.1+cu121.

Install PyTorch e.g.

pip --no-cache-dir install torch==1.11.0+cu113  torchvision==0.12.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113

Install Dependencies

pip install -r ./requirements.txt
pip install av2

Download Download Argoverse 2 Motion Forecasting Dataset

The expected structure of the AV2 data should be:

data_root
    ├── train
    │   ├── 0000b0f9-99f9-4a1f-a231-5be9e4c523f7
    │   ├── 0000b6ab-e100-4f6b-aee8-b520b57c0530
    │   ├── ...
    ├── val
    │   ├── 00010486-9a07-48ae-b493-cf4545855937
    │   ├── 00062a32-8d6d-4449-9948-6fedac67bfcd
    │   ├── ...
    ├── test
    │   ├── 0000b329-f890-4c2b-93f2-7e2413d4ca5b
    │   ├── 0008c251-e9b0-4708-b762-b15cb6effc27
    │   ├── ...

Data Preprocessing

Preprocess the Argoverse 2 dataset by executing

python preprocess.py --data_root=/path/to/data_root -p

Training

Train EMP-M/D model using

python train.py data_root=/path/to/data_root model=emp gpus=1 batch_size=96 monitor=val_minFDE6 model.target.decoder=mlp

Use model.target.decoder=mlp for EMP-M and model.target.decoder=detr for EMP-D.

Evaluation

Run evaluation using

python eval.py data_root=/path/to/data_root batch_size=32 'checkpoint="/path/to/checkpoint.ckpt"'

Evaluation

To visualize scenario data and model predictions use

python visualize.py -p

Please set the datafolder, split and checkpoint directly in the visualize.py script.

Without -p, only the input data is visualized.

Pretrained Model Weights

You can find our pretrained weights for AV2 in the checkpoints folder.

• EMP-M
• EMP-D

Bibtex

@inproceedings{prutsch2024efficient,
 title={{Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed}},
 author={Alexander Prutsch, Horst Bischof, Horst Possegger},
 booktitle={IROS},
 year={2024},
}

Acknowledgements

This repository is based on Forecast-MAE. We thank them for their excellent work!