Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166719
Title: Intention prediction-based control for vehicle platoon to handle driver cut-In
Authors: Yun, Lu
Huang, Lingying
Yao, Jiarong
Su, Rong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2023
Source: Yun, L., Huang, L., Yao, J. & Su, R. (2023). Intention prediction-based control for vehicle platoon to handle driver cut-In. IEEE Transactions On Intelligent Transportation Systems. https://dx.doi.org/10.1109/TITS.2023.3239606
Project: A19D6a0053 
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: Vehicle platoons (VPs) are groups of vehicles driving together with a short inter-vehicle gap and a harmonized velocity. For a long period, the VPs and human-driven vehicles (HDVs) will coexist in mixed traffic flow, where the cut-in maneuver of the HDVs towards the VPs can be frequently expected. In this paper, to handle such cut-ins, we propose an intention prediction-based control method for the VPs by considering the tradeoff between the platoon integrity and traffic safety. Particularly, the proposed method is designed to prevent as many cut-ins as possible while taking care of the road safety. It consists of a cut-in prediction part, including intention and trajectory prediction algorithms, and a finite state machine (FSM)-based predictive control part, including a high-level FSM and a low-level predictive control. Driver-in-the-loop experiments were conducted in the VP-based driving scenarios to train the intention prediction algorithm and test the proposed method. We show the results detailing the control behavior of the proposed method in a no cut-in test, a mandatory cut-in test, and three discretionary cut-in tests. The results demonstrate that the proposed method can predict the cut-in intention of human drivers in real time. Besides, according to the prediction results, the proposed method can prevent cut-ins for the VPs while taking care of the road safety.
URI: https://hdl.handle.net/10356/166719
ISSN: 1524-9050
DOI: 10.1109/TITS.2023.3239606
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2023.3239606.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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