Improving-Efficiency-and-Generalisability-of-Motion-Predictions-with-Deep-Multi-Agent-Learning-and-Multi-Head-Attention (the repo is being updated ...)

This work aims to design a data-driven prediction framework for highly Automated Vehicles (AVs) that utilises multiple inputs to prove a multimodal, probabilistic estimate of the future intentions and trajectories of surrounding vehicles in freeway operation. Our proposed framework is a deep multi- agent learning-based system designed to effectively capture social interactions (please refer to our recent paper between vehicles without relying on map information. This algorithm achieved a good prediction performance with a lower prediction error in real traffic data at highways. Evaluation of the proposed framework using the NGSIM (US-101 and I-80) and HighD datasets shows satisfactory prediction performance for long- term trajectory prediction of multiple surrounding vehicles. Additionally, the proposed framework has higher prediction accuracy and generalisability than state-of-the-art approaches.

Djamel Eddine Benrachou

Centre for Accident Research & Road Safety - Queensland, QUT

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News 🚀🚀

• [12/2023] Work on Transactions on Intelligent Transportation Systems (T-ITS, Q1-JCR): "Improving Efficiency and Generalisability of Motion Predictions With Deep Multi-Agent Learning and Multi-Head Attention"
• [09/2022] Work on Transactions on Intelligent Transportation Systems (T-ITS, Q1-JCR): "Use of Social Interaction and Intention to Improve Motion Prediction Within Automated Vehicle Framework: A Review"

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This is the official repository and PyTorch implementations of different works presented at:

Our papers:

• Improving Efficiency and Generalisability of Motion Predictions with Multi-Agent Learning and Multi-Head Attention in IEEE Transactions on Intelligent Transportation Systems
• Use of Social Interaction and Intention to Improve Motion Prediction Within Automated Vehicle Framework: A Review in IEEE Transactions on Intelligent Transportation Systems

Algorithm:

Setup：

The code can be executed in a conda environment--please follow the tutorial below

conda create -n MyEnvi python=3.7
source activate MyEnvi

conda install pytorch==1.12.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch
conda install tensorboard=1.14.0
conda install numpy=1.16 scipy=1.4 h5py=2.10 future

The code can also be written in the following environment:

python 3.7.11
pytorch 1.10.0
cuda 11.3 or above
cudnn 8.2.0

If available, check requirements.txt

conda create --name GATLSTM_env python=3.7 \
conda install -n GATLSTM_env ipykernel --update-deps --force-reinstall

python3 -m pip install --upgrade pip \
python3 -m pip install --upgrade Pillow \

pip install \
    prodict \
    torch \
    pyyaml \
    torchvision \
    tensorboard \
    glob2 \
    matplotlib \
    sklearn \
    gitpython \
    thop \
    fvcore \
    torchstat \
    torchsummary \
    ipykernel \
    sns

Datasets

NGSIM

From NGSIM website:

• Register at NGSIM
• Download [NGSIM-US-101-LosAngeles-CA]US-101-LosAngeles-CA.zip and [NGSIM-I-80-Emeryville-CA] I-80-Emeryville-CA.zip
• Unzip and extract vehicle-trajectory-data into raw/us-101 and ./raw/i-80

From googledrive:

• Download i-80: https://drive.google.com/open?id=19ovxiJLCnS1ar1dYvUIrfdz_UnGejK-k
• Download us-101: https://drive.google.com/open?id=14dMKew22_5evfOoSGEBYqHP3L92e7nyJ

Dataset fields:

• doc/trajectory-data-dictionary.htm

This Dataset is to be pre-processed with the Matlab function preprocess_data.m (that is a slightly modified version of the one from https://github.com/nachiket92/conv-social-pooling)

• Already preprocessed samples: (to run tests on Hybrid-Models)
  • https://drive.google.com/open?id=171YZvK2DnJbtIgve6qAOkk6AFNrGAYTJ
  • https://drive.google.com/open?id=1N080iiQc43MTvLMqaLtsZh49okJL0hPx

Dataset fields:

• doc/trajectory-data-dictionary.htm

HighD

• https://www.highd.com/

Pre-processed NGSIM and HighD can be downloaded from here

Data Preparation: The raw data of Next Generation Simulation (NGSIM) is downloadable at https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm

1. Download the data: Once the data downloaded the raw data into ./raw
2. Preprocess the data : Run

preprocess_data.m to pre-preprocess the raw data (note: the pre-processed data will be uploaded on cloud).

This repository is for testing the trained models for motion prediction on highways. No end-to-end trainer is needed here. 3.To use the pretrained models at ./ .py and evaluate the models performance run! 🎉 🎉

python3 evaluate.py --name ngsim_model --batch_size 64 \
    --test_set ./datasets/NGSIM/test.mat 

Trained models can be downloaded here

Citation

Please cite our papers if you used our code. Thanks.

@article{benrachou2022use,
  title={Use of social interaction and intention to improve motion prediction within automated vehicle framework: A review},
  author={Benrachou, Djamel Eddine and Glaser, Sebastien and Elhenawy, Mohammed and Rakotonirainy, Andry},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2022},
  publisher={IEEE}
}

@article{benrachou2023improving,
  title={Improving Efficiency and Generalisability of Motion Predictions With Deep Multi-Agent Learning and Multi-Head Attention},
  author={Benrachou, Djamel Eddine and Glaser, Sebastien and Elhenawy, Mohammed and Rakotonirainy, Andry},
  journal={IEEE Transactions on Intelligent Transportation Systems},
  year={2023},
  publisher={IEEE}
}

License

LICENSE