we focus on traffic scenarios where a connected and autonomous vehicle (CAV) serves as the central agent, utilizing both sensors and communication technologies to perceive its surrounding traffics consisting of autonomous vehicles, connected vehicles, and human-driven vehicles.
1. Clone this repository:
git clone https://github.com/xichennn/MSMA.git
cd MSMA
2. Create a conda environment and install the dependencies:
conda create -n MSMA python=3.8
conda activate MSMA
conda install pytorch==1.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
# install other dependencies
pip install pytorch-lightning
pip install torch-scatter torch-geometric -f https://pytorch-geometric.com/whl/torch-2.1.0+cu121.html
3. Download the CARLA simulation data and move it to the carla_data dir.
In train.py, There are 3 hyperparameters that control the data processing:
- mpr: determines the mpr of the connected vehicles in the dataset
- delay_frame: determines the latency ranging from 1 to 15 frames (0.1~1.5s)
- noise_var: determines the Gaussian noise variance ranging from 0 to 0.5 \
and there are two in the model arguments that control the data fusion:
- commu_only: when set to true, only data from connected vehicles are utilized
- sensor_only: when set to true, only data from AV sensors are utilized
when both commu_only and sensor_only are set to False, data from both sources will be integrated
| Metrics | MPR=0 | MPR=0.2 | MPR=0.4 | MPR=0.6 | MPR=0.8 |
|---|---|---|---|---|---|
| ADE | 0.62 | 0.61 | 0.59 | 0.59 | 0.56 |
| FDE | 1.48 | 1.47 | 1.40 | 1.37 | 1.33 |
| MR | 0.23 | 0.22 | 0.22 | 0.21 | 0.20 |
| MPR=0 | MPR=0.4 | MPR=0.8 |
|---|---|---|
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If you found this repository useful, please cite as:
@article{chen2024msma,
title={MSMA: Multi-agent Trajectory Prediction in Connected and Autonomous Vehicle Environment with Multi-source Data Integration},
author={Chen, Xi and Bhadani, Rahul and Sun, Zhanbo and Head, Larry},
journal={arXiv preprint arXiv:2407.21310},
year={2024}
}
This repository is licensed under Apache 2.0.