Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties

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dc.contributor.authorLi, Jie
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorPan, Wenjun
dc.contributor.authorLiu, Yonggang
dc.contributor.authorZhang, Yuanjian
dc.contributor.authorChen, Zheng
dc.date.accessioned2023-07-05T11:18:31Z
dc.date.available2023-07-05T11:18:31Z
dc.date.issued2023-06-19
dc.description.abstractEco-driving control poses great energy-saving potential at multiple signalized intersection scenarios. However, traffic uncertainties can often lead to errors in ecological velocity planning and result in increased energy consumption. This study proposes an eco-driving approach with a hierarchical framework to be leveraged at signalized intersections that considers the impact of traffic uncertainty. The proposed approach leverages a queue-based traffic model in the upper level to estimate the impact of traffic uncertainty and generate dynamic modified traffic light information. In the lower level, a deep reinforcement learning-based controller is constructed to optimize velocity subject to the constraints from the traffic lights and traffic uncertainty, thereby reducing energy consumption while ensuring driving safety. The effectiveness of the proposed control strategy is demonstrated through numerous simulation case studies. The simulation results show that the proposed method significantly improves energy economy and prevents unnecessary idling in uncertain traffic scenarios, as compared to other approaches that ignore traffic uncertainty. Furthermore, the proposed method is adaptable to different traffic scenarios and showcases energy efficiency.en_UK
dc.identifier.citationLi J, Fotouhi A, Pan W, et al., (2023) Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties, Energy, Volume 279, September 2023, Article Number 128139en_UK
dc.identifier.eissn1873-6785
dc.identifier.issn0360-5442
dc.identifier.urihttps://doi.org/10.1016/j.energy.2023.128139
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19930
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEco-drivingen_UK
dc.subjectDeep reinforcement learningen_UK
dc.subjectVelocity optimizationen_UK
dc.subjectSignalized intersectionen_UK
dc.subjectConnected electric vehicleen_UK
dc.titleDeep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertaintiesen_UK
dc.typeArticleen_UK

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