Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

TL;DR

This paper proposes a deep reinforcement learning framework for lane merge coordination in connected vehicles, utilizing a centralised Traffic Orchestrator and Data Fusion to generate adaptive trajectory recommendations.

Contribution

It introduces a novel framework combining deep reinforcement learning with data fusion for lane merge coordination in connected vehicles, demonstrating adaptability in real-world scenarios.

Findings

Dueling Deep Q-Network effectively manages unseen merging scenarios.

Performance comparison shows the superiority of the proposed RL models.

The framework improves lane merge efficiency and safety.

Abstract

In this paper, a framework for lane merge coordination is presented utilising a centralised system, for connected vehicles. The delivery of trajectory recommendations to the connected vehicles on the road is based on a Traffic Orchestrator and a Data Fusion as the main components. Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles, taking into account unconnected vehicles for those suggestions. The results highlight the adaptability of the Traffic Orchestrator, when employing Dueling Deep Q-Network in an unseen real world merging scenario. A performance comparison of different reinforcement learning models and evaluation against Key Performance Indicator (KPI) are also presented.