SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving

Introduction

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, lzhangbz@connect.ust.hk),

which is accepted for publication in the IEEE Robotics and Automation Letters (RA-L), 2024.

Preprint: arXiv

Video: YouTube

Qualitative Results

• On Argoverse 1 motion forecasting dataset

  

• On Argoverse 2 motion forecasting dataset

  

Todo List

• [x] Release code for Argoverse 2 dataset
• [x] Release training and evaluation scripts for DDP
• [x] First release

Gettting Started

Install dependencies

• 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

Play with pretrained models (Argoverse 1)

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

Train from scratch

• 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

### Train/Evaluate/Visualize SIMPL using Argoverse 2
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.

## Acknowledgment
We would like to express sincere thanks to the authors of the following packages and tools:
- [LaneGCN](https://github.com/uber-research/LaneGCN)
- [HiVT](https://github.com/ZikangZhou/HiVT)
- [DSP](https://github.com/HKUST-Aerial-Robotics/DSP)

## License
This repository is licensed under [MIT license](https://github.com/HKUST-Aerial-Robotics/SIMPL/blob/main/LICENSE).