Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced Data Processing

Ruo Chen, Yang Zhu, Yang Zhu, Hongye Su, Hongye Su

2025

Abstract

In this paper, we use deep reinforcement learning to enable connected and automated vehicles (CAVs) to drive in a intersection with human-driven vehicles. The multi-agent deep deterministic policy gradient (MADDPG) algorithm is improved to be more efficient for data processing, so that it can solve the problem of learning bottlenecks in complex environments, and use sliding control to execute control strategies. Finally, the feasibility of the method is verified in the simulation environment of CARLA.

Paper Citation

in Harvard Style

Chen R., Zhu Y. and Su H. (2025). Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced Data Processing. In Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-745-0, SciTePress, pages 294-300. DOI: 10.5220/0013133100003941

in Bibtex Style

@conference{vehits25,
author={Ruo Chen and Yang Zhu and Hongye Su},
title={Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced Data Processing},
booktitle={Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2025},
pages={294-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013133100003941},
isbn={978-989-758-745-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced Data Processing
SN - 978-989-758-745-0
AU - Chen R.
AU - Zhu Y.
AU - Su H.
PY - 2025
SP - 294
EP - 300
DO - 10.5220/0013133100003941
PB - SciTePress