Vehicle platooning is a critical technology for transportation efficiency and energy consumption reduction. Even though conventional solutions to generate simultaneous lane-changing strategies have been widely studied, when managing platoons on freeways with mixed traffic, it becomes challenging to support sequential lane-changing maneuvers, which can perform efficient lane-changing even in high traffic density. To achieve that, this paper proposes an integrated approach called SmartPL, which can: 1) maintain close and efficient driving behaviors with stable platoon inter-vehicle distance during lane changes through a hybrid control model that separately manages car-following and lane-changing behaviors; 2) produce foresighted strategies by roadside unit (RSU) deploying a dedicated neural network, which comprises a traffic information encoder, a spatial relation extractor, and a strategy maker; and 3) facilitate sequential and safe lane-changing maneuvers by implementing a safety monitor on each platoon member. Furthermore, the efficiency and effectiveness of SmartPL are evaluated in a mixed-traffic freeway simulation. SmartPL achieves superior performance in accelerating and safeguarding lane-changing maneuvers, with average improvements of 12.54%, 14.66%, and 22.94% over three state-of-the-art methods, respectively. Code is available at https://github.com/IntelligentSystemsLab/SmartPL.


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    Title :

    SmartPL: An Integrated Approach for Platoons Driving on Mixed-Traffic Freeways


    Additional title:

    Communic.Comp.Inf.Science


    Contributors:
    Mahmud, Mufti (editor) / Doborjeh, Maryam (editor) / Wong, Kevin (editor) / Leung, Andrew Chi Sing (editor) / Doborjeh, Zohreh (editor) / Tanveer, M. (editor) / Li, Han (author) / You, Linlin (author) / Xie, Jieming (author) / Ye, Yun (author)

    Conference:

    International Conference on Neural Information Processing ; 2024 ; Auckland, New Zealand December 02, 2024 - December 06, 2024



    Publication date :

    2025-06-24


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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