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

Journal article (2018)

Authors

Yihuan Zhang Tongji University
Q. Lin Cyber Security - EEMCS
Jun Wang Tongji University
S.E. Verwer Cyber Security - EEMCS
John M. Dolan Carnegie Mellon University
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2018 Yihuan Zhang, Q. Lin, Jun Wang, S.E. Verwer, John M. Dolan
DOI
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https://doi.org/10.1109/TIV.2018.2843178
Model predictive control Lane change prediction Cooperative car-following Driving behavior estimation
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

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.