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 dataset of Next Generation Simulation are 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.
BibTeX
@article{Zhang-2018-116171,author = {Yihuan Zhang and Qin Lin and Jun Wang and Sicco Verwer and John M. Dolan},
title = {Lane-change Intention Estimation for Car-following Control in Autonomous Driving},
journal = {IEEE Transactions on Intelligent Vehicles},
year = {2018},
month = {September},
volume = {3},
number = {3},
pages = {276 - 286},
keywords = {cooperative car-following, driving behavior estimation, lane change prediction, model predictive control},
}