LMMCoDrive: Cooperative Driving with Large Multimodal Model

This repository contains the code implementation for the paper titled LMMCoDrive: Cooperative Driving with Large Multimodal Model by Haichao Liu, Ruoyu Yao, Zhenmin Huang, Shaojie Shen, and Jun Ma.

Abstract

To address the challenges of decentralized cooperative scheduling and motion planning in Autonomous Mobility-on-Demand (AMoD) systems, this paper introduces LMMCoDrive, a novel cooperative driving framework that utilizes a Large Multimodal Model (LMM) to enhance traffic efficiency in dynamic urban environments.

Code Implementation

The code in this repository accompanies the research paper and includes the following key components:

• Scheduling and Motion Planning Integration: The code integrates scheduling and motion planning processes for Cooperative Autonomous Vehicles (CAVs).

• Decentralized Optimization Algorithm: Utilizes the Alternating Direction Method of Multipliers (ADMM) within the LMM framework for graph evolution to drive the cooperative motion planning of CAVs.

• Simulation and Experimental Results: Demonstrates the effectiveness of LMMCoDrive in optimizing CAV scheduling and enhancing cooperative driving tasks in CARLA 0.9.14.

Usage

To use the code in this repository, follow these steps:

1. Clone the repository: git clone https://github.com/henryhcliu/LMMCoDrive.git
2. Create a new Conda environment (Python 3.8 is recommended) and activate it.
3. Install the necessary dependencies by pip install -r requirements.txt
4. Run the CARLA simulator in the Terminal using ./CarlaUE4.sh in the corresponding CARLA folder to launch the server
5. Run the main script by python main_lmmcodrive.py to see the implementation in action.

Citation

If you find this code or research paper helpful, please consider citing:

@article{liu2024lmmcodrive,
  title={LMMCoDrive: Cooperative Driving with Large Multimodal Model},
  author={Liu, Haichao and Yao, Ruoyu and Huang, Zhenmin and Shen, Shaojie and Ma, Jun},
  journal={arXiv preprint arXiv:2409.11981},
  year={2024}
}

For more details, refer to the paper available here.

For any questions or issues regarding the code, feel free to open an issue in this repository.