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.
SmartPL: An Integrated Approach for Platoons Driving on Mixed-Traffic Freeways
Communic.Comp.Inf.Science
International Conference on Neural Information Processing ; 2024 ; Auckland, New Zealand December 02, 2024 - December 06, 2024
24.06.2025
16 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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