A common pipeline of trajectory prediction models and the proposed G2LTraj.

Environment
All models were trained and tested on Ubuntu 18.04 with Python 3.7 and PyTorch 1.12.1 with CUDA 11.1.

Dataset
Preprocessed ETH and UCY datasets are included in this repository, under ./datasets/. The train/validation/test splits are the same as those fond in Social-GAN.

You can also download the dataset by running the following script.

./scripts/download_datasets.sh

Baseline models
This repository supports six baseline models: AgentFormer, DMRGCN, Graph-TERN, PECNet, SGCN and Social-STGCNN. We have included model source codes from their official GitHub in the ./baselines/ folder.

To train our G2LTraj on the ETH and UCY datasets at once, we provide a bash script train.sh for a simplified execution.

./scripts/train.sh

We provide additional arguments for experiments:

./scripts/train.sh -t <experiment_tag> -b <baseline_model> -c <config_file_path> -p <config_file_prefix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

# Supported baselines: agentformer, dmrgcn, graphtern, pecnet, sgcn, stgcnn
# Supported datasets: eth, hotel, univ, zara1, zara2

# Examples
./scripts/train.sh -b sgcn -d "hotel" -i "1"
./scripts/train.sh -b agentformer -t agentformer -d "zara2" -i "2"
./scripts/train.sh -b pecnet -c ./config/  -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"

If you want to train the model with custom hyper-parameters, use trainval.py instead of the script file.

python trainval.py --cfg <config_file_path> --tag <experiment_tag> --gpu_id <gpu_id> 

We will provide all pretrained models in the checkpoints folder.

To evaluate our G2LTraj at once, we provide a bash script test.sh for a simplified execution.

./scripts/test.sh -t <experiment_tag> -b <baseline_model> -c <config_file_path> -p <config_file_prefix> -d <space_seperated_dataset_string> -i <space_seperated_gpu_id_string>

# Examples
./scripts/test.sh -b sgcn -d "hotel" -i "1"
./scripts/test.sh -b agentformer -t agentformer -d "zara2" -i "2"
./scripts/test.sh -b pecnet -c ./config/ -d "eth hotel univ zara1 zara2" -i "0 0 0 0 0"

If you want to evaluate the model individually, you can use trainval.py with custom hyper-parameters.

python trainval.py --test --cfg <config_file_path> --tag <experiment_tag> --gpu_id <gpu_id> 

If you find our work useful in your research, please cite our paper G2LTraj:

@inproceedings{ijcai2024p285,
  title     = {G2LTraj: A Global-to-Local Generation Approach for Trajectory Prediction},
  author    = {Zhang, Zhanwei and Hua, Zishuo and Chen, Minghao and Lu, Wei and Lin, Binbin and Cai, Deng and Wang, Wenxiao},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {2579--2587},
  year      = {2024}
}

Our code builds upon EigenTrajectory. We appreciate all the contributors for their awesome work.