@inproceedings{edb57fa3425244b796fc9875b4b9c550,
title = "Research on Intelligent Merging Decision-making of Unmanned Vehicles Based on Reinforcement Learning",
abstract = "The decision-making model of merging behavior is one of the key technologies of unmanned vehicles. In order to solve the problem of unmanned vehicles' merging decision making, this paper presents a merging strategy based on Least squares Policy Iteration (LSPI) algorithm, and selects the basis function which includes reciprocal of TTC, relative distance and relative speed to represent state space and discretizes action space. This study synthetically takes consideration o safety, the success of the task, the merging efficiency and comfort in setting reward function, compares the Q-learning with LSPI algorithm, and verifies its adaptability by using NGSIM data. The algorithm can ultimately achieve a success rate of 86%. This research can provide theoretic support and technical basis for the merging decision-making of unmanned vehicles.",
author = "Chen, {Xue Mei} and Qiang Zhang and Zhang, {Zhen Hua} and Liu, {Ge Meng} and Gong, {Jian Wei} and Chan, {Ching Yao}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Intelligent Vehicles Symposium, IV 2018 ; Conference date: 26-09-2018 Through 30-09-2018",
year = "2018",
month = oct,
day = "18",
doi = "10.1109/IVS.2018.8500706",
language = "English",
series = "IEEE Intelligent Vehicles Symposium, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "91--96",
booktitle = "2018 IEEE Intelligent Vehicles Symposium, IV 2018",
address = "United States",
}