Standard

Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving. / Zhang, Yihuan; Lin, Qin; Wang, Jun; Verwer, Sicco; Dolan, John M.

In: IEEE Transactions on Intelligent Vehicles, Vol. 3, No. 3, 2018, p. 276-286.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Zhang, Y, Lin, Q, Wang, J, Verwer, S & Dolan, JM 2018, 'Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving' IEEE Transactions on Intelligent Vehicles, vol. 3, no. 3, pp. 276-286. https://doi.org/10.1109/TIV.2018.2843178

APA

Zhang, Y., Lin, Q., Wang, J., Verwer, S., & Dolan, J. M. (2018). Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving. IEEE Transactions on Intelligent Vehicles, 3(3), 276-286. https://doi.org/10.1109/TIV.2018.2843178

Vancouver

Zhang Y, Lin Q, Wang J, Verwer S, Dolan JM. Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving. IEEE Transactions on Intelligent Vehicles. 2018;3(3):276-286. https://doi.org/10.1109/TIV.2018.2843178

Author

Zhang, Yihuan ; Lin, Qin ; Wang, Jun ; Verwer, Sicco ; Dolan, John M. / Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving. In: IEEE Transactions on Intelligent Vehicles. 2018 ; Vol. 3, No. 3. pp. 276-286.

BibTeX

@article{a958f938337642eab0df55cd6d3fc18c,
title = "Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving",
abstract = "Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.",
keywords = "Cooperative car-following, driving behavior estimation, lane change prediction, model predictive control",
author = "Yihuan Zhang and Qin Lin and Jun Wang and Sicco Verwer and Dolan, {John M.}",
note = "Accepted author manuscript",
year = "2018",
doi = "10.1109/TIV.2018.2843178",
language = "English",
volume = "3",
pages = "276--286",
journal = "IEEE Transactions on Intelligent Vehicles",
issn = "2379-8858",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

RIS

TY - JOUR

T1 - Lane-Change Intention Estimation for Car-Following Control in Autonomous Driving

AU - Zhang, Yihuan

AU - Lin, Qin

AU - Wang, Jun

AU - Verwer, Sicco

AU - Dolan, John M.

N1 - Accepted author manuscript

PY - 2018

Y1 - 2018

N2 - Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.

AB - Car-following is the most general behavior in highway driving. It is crucial to recognize the cut-in intention of vehicles from an adjacent lane for safe and cooperative driving. In this paper, a method of behavior estimation is proposed to recognize and predict the lane change intentions based on the contextual traffic information. A model predictive controller is designed to optimize the acceleration sequences by incorporating the lane-change intentions of other vehicles. The public data set of next generation simulation is labeled and then published as a benchmarking platform for the research community. Experimental results demonstrate that the proposed method can accurately estimate vehicle behavior and therefore outperform the traditional car-following control.

KW - Cooperative car-following

KW - driving behavior estimation

KW - lane change prediction

KW - model predictive control

U2 - 10.1109/TIV.2018.2843178

DO - 10.1109/TIV.2018.2843178

M3 - Article

VL - 3

SP - 276

EP - 286

JO - IEEE Transactions on Intelligent Vehicles

T2 - IEEE Transactions on Intelligent Vehicles

JF - IEEE Transactions on Intelligent Vehicles

SN - 2379-8858

IS - 3

ER -

ID: 47448267