This is the project page of the paper
- Lu Zhang, Peiliang Li, Sikang Liu, and Shaojie Shen, "SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving", arXiv preprint arXiv:2402.02519 (2024), (Corresponding author: Lu ZHANG, [email protected]),
which is accepted for publication in the IEEE Robotics and Automation Letters (RA-L), 2024.
Preprint: arXiv
Video: YouTube
- On Argoverse 1 motion forecasting dataset
- On Argoverse 2 motion forecasting dataset
- Release code for Argoverse 2 dataset
- Release training and evaluation scripts for DDP
- First release
- Create a new conda virtual env
conda create --name simpl python=3.8
conda activate simpl
- Install PyTorch according to your CUDA version. We recommend CUDA >= 11.1, PyTorch >= 1.8.0.
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.6 -c pytorch -c conda-forge
-
Install Argoverse 1 & 2 APIs, please follow argoverse-api and av2-api.
-
Install other dependencies
pip install scikit-image IPython tqdm ipdb tensorboard
Generate a subset of the dataset for testing using the script. It will generate 1k samples to data_argo/features/:
sh scripts/argo_preproc_small.sh
The dataset directory should be organized as follows:
data_argo
├── features
│  ├── train
│  │  ├── 100001.pkl
│  │  ├── 100144.pkl
│  │  ├── 100189.pkl
...
│  └── val
│  ├── 10018.pkl
│  ├── 10080.pkl
│  ├── 10164.pkl
...
The pre-trained weights are located at saved_models/. Use the script below to visualize prediction results:
sh scripts/simpl_av1_vis.sh
Since we store each sequence as a single file, the system may raise error OSError: [Erron 24] Too many open files during evaluation and training. You may use the command below to solve this issue:
ulimit -SHn 51200
ulimit -s unlimited
To evaluate the trained models:
sh scripts/simpl_av1_eval.sh
You are supposed to get:
Validation set finish, cost 289.01 secs
-- minade_1: 1.428 minfde_1: 3.240 mr_1: 0.512 brier_fde_1: 3.240 minade_k: 0.658 minfde_k: 0.947 mr_k: 0.081 brier_fde_k: 1.558
- Preprocess full Argoverse 1 motion forecasting dataset using the script:
sh scripts/argo_preproc_all.sh
The preprocessed dataset will cost about 15 GB storage, please reserve enough space for preprocessing.
- Launch training using the script:
# single-GPU
sh scripts/simpl_av1_train.sh
# multi-GPU based on DDP
sh scripts/simpl_av1_train_ddp.sh
- For model evaluation, please refer to the following scripts:
# single-GPU
sh scripts/simpl_av1_eval.sh
# multi-GPU based on DDP
sh scripts/simpl_av1_eval_ddp.sh
Please refer to the scripts in the directory scripts/, and the usage is similar to scripts for the Argoverse 1 dataset. If you have any questions, please feel free to raise an issue or contact us via email.
We would like to express sincere thanks to the authors of the following packages and tools:
This repository is licensed under MIT license.