Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

• URL: http://arxiv.org/abs/2010.10567v1
• Date: Tue, 20 Oct 2020 19:01:51 GMT
• Title: Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles
• Authors: Omar Nassef, Luis Sequeira, Elias Salam and Toktam Mahmoodi
• Abstract summary: The framework is based on a Traffic Orchestrator and a Data Fusion. Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles. The results highlight the adaptability of the Traffic Orchestrator, when employing Dueling Deep Q-Network in an unseen real world merging scenario.
• Score: 1.2387676601792896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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