Open AccessData‐driven human‐like cut‐in driving model using generative adversarial network
Author(s) -
Yun Y.,
Jeong D.,
Lim S.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2019.2122
Subject(s) - divergence (linguistics) , generative adversarial network , generative grammar , adversarial system , computer science , generative model , artificial intelligence , artificial neural network , machine learning , deep learning , philosophy , linguistics
In this Letter, the authors developed a data‐driven human‐like cut‐in driving model using a generative adversarial network (GAN). When a vehicle cuts into the lane of another vehicle, complex interactions occur among the various vehicles. The purpose of the GAN driver model is to learn the human‐driving abilities in complex and diverse situations. Using the Kullback–Leibler divergence evaluation method, the authors confirmed that the GAN driver model shows more human‐like driving than the rule‐based driver model.