Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach

TL;DR

This paper introduces a Multi-Agent Reinforcement Learning framework for autonomous vehicles to navigate complex, dynamic roadblocks efficiently, leveraging 6G communication to enhance traffic flow and safety in intelligent transportation systems.

Contribution

It presents a novel MARL approach combined with 6G-V2X communication to improve AV decision-making in dynamic environments, incorporating realistic constraints and extensive simulation testing.

Findings

Achieves over 70% efficiency in dynamic roadblock navigation.

Demonstrates adaptability to various traffic conditions.

Outperforms benchmark solutions in traffic flow management.

Abstract

Autonomous Vehicles (AVs) represent a transformative advancement in the transportation industry. These vehicles have sophisticated sensors, advanced algorithms, and powerful computing systems that allow them to navigate and operate without direct human intervention. However, AVs' systems still get overwhelmed when they encounter a complex dynamic change in the environment resulting from an accident or a roadblock for maintenance. The advanced features of Sixth Generation (6G) technology are set to offer strong support to AVs, enabling real-time data exchange and management of complex driving maneuvers. This paper proposes a Multi-Agent Reinforcement Learning (MARL) framework to improve AVs' decision-making in dynamic and complex Intelligent Transportation Systems (ITS) utilizing 6G-V2X communication. The primary objective is to enable AVs to avoid roadblocks efficiently by changing lanes while maintaining optimal traffic flow and maximizing the mean harmonic speed. To ensure realistic operations, key constraints such as minimum vehicle speed, roadblock count, and lane change frequency are integrated. We train and test the proposed MARL model with two traffic simulation scenarios using the SUMO and TraCI interface. Through extensive simulations, we demonstrate that the proposed model adapts to various traffic conditions and achieves efficient and robust traffic flow management. The trained model effectively navigates dynamic roadblocks, promoting improved traffic efficiency in AV operations with more than 70% efficiency over other benchmark solutions.