@inproceedings{42ee87db66ae438da12d252fda7d8e89,
title = "Neural network on-line modeling for mechanically coupled vehicle",
abstract = "This study was conducted to increase the usefulness of personal vehicles. Single-occupant personal vehicles are easy to handle, but their load capacities are smaller than other types of vehicles. This paper presents a solution to this problem in the form of a system for vehicles that couple mechanically. However, if vehicles are only coupled, their performance in braking, accelerating, and steering is degraded. The proposed system employs a neural network algorithm, constructs the whole coupled-vehicle model automatically while the vehicles are being driven, and makes drivers feel as if they are driving stand-alone vehicles. In this paper, we present the details of the proposed method and the results of computer simulation experiments that demonstrate the effectiveness of the system.",
keywords = "Coupled vehicle, Machine learning, Neural network, On-line modeling, Personal vehicle, Platoon",
author = "Takeki Ogitsu and Tokunosuke Ikegami and Shin Kato and Hiroshi Mizoguchi",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014 ; Conference date: 21-10-2014 Through 23-10-2014",
year = "2014",
doi = "10.1109/EMS.2014.99",
language = "English",
series = "Proceedings - UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "27--32",
editor = "David Al-Dabass and Marco Vannucci and Athanasios Pantelous and Valentina Colla",
booktitle = "Proceedings - UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014",
}