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

2024-12-31

AI Summary

To address traffic congestion caused by dynamic road obstacles (e.g., accidents, construction zones) in 6G-enabled V2X autonomous driving environments, this paper proposes a constraint-aware multi-agent reinforcement learning (MARL) framework for cooperative optimization. Methodologically, it introduces hard constraints—including minimum speed, obstacle density, and lane-changing frequency—into a modified MADDPG architecture, while tightly integrating ultra-low-latency 6G V2X communication with high-fidelity SUMO/TraCI traffic simulation. The key contribution lies in jointly ensuring individual agent robustness against obstacles and collective traffic flow stability. Experimental evaluation across two realistic scenarios demonstrates that the proposed approach improves traffic throughput by over 70% compared to baseline methods, significantly increases average harmonic speed, and enables millisecond-level dynamic response and adaptive cooperative control.

Technology Category

• Multiagent Systems: Adversarial Agents
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Autonomous Vehicles
• Traffic Efficiency
• Driving Constraints

Innovation

• Multi-Agent Reinforcement Learning (MARL)
• 6G-V2X Communication Technology
• Autonomous Driving Decision Optimization