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Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Omar Nassef, Luis Sequeira Villarreal, Elias Salam, Toktam Mahmoodi

Original languageEnglish
Title of host publicationIEEE PIMRC 2020
Accepted/In press2020

Documents

  • PIMRC20_Lane-merge_RL

    PIMRC20_Lane_merge_RL.pdf, 1.07 MB, application/pdf

    Uploaded date:20 Jun 2020

    Version:Accepted author manuscript

King's Authors

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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