Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166720
Title: Modeling of driver cut-in behavior towards a platoon
Authors: Lu, Yun
Wang, Bohui
Huang, Lingying
Zhao, Nanbin
Su, Rong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Lu, Y., Wang, B., Huang, L., Zhao, N. & Su, R. (2022). Modeling of driver cut-in behavior towards a platoon. IEEE Transactions On Intelligent Transportation Systems, 23(12), 24636-24648. https://dx.doi.org/10.1109/TITS.2022.3202494
Project: A19D6a0053 
Journal: IEEE Transactions on Intelligent Transportation Systems 
Abstract: A vehicle platoon is a group of vehicles driving together with a harmonized speed and a short inter-vehicle gap by using vehicle automation and vehicle-to-vehicle communication. Platoons have to share road with human-driven vehicles (HDVs) and can only be applied in heterogeneous traffic flow for a long period. Driver cut-in behavior (DCB) towards a platoon can be frequently expected in such driving context. In this paper, to understand and simulate such behavior, we propose a platoon-oriented cut-in behavior (POCB) model by fusing a lateral and a longitudinal control model into the queuing network (QN) cognitive architecture. Platoon-oriented cut-in experiments are conducted to collect driver data under cut-in from back and front scenarios, which both include six sub-scenarios with different platoon gaps or initial velocities. We demonstrate the effectiveness of the proposed model in simulating the DCB towards platoons by comparing experimental and simulation results under various driving scenarios across different subjects.
URI: https://hdl.handle.net/10356/166720
ISSN: 1524-9050
DOI: 10.1109/TITS.2022.3202494
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 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.2022.3202494.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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