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Authors: Jae-Cheon Lee 1 ; Hao Liu 1 ; Young Seo 2 ; Seong Kwak 2 ; Ho Lee 1 ; Hae Jo 1 and Sangsu Park 2

Affiliations: 1 Department of Mechanical and Automotive Engineering, Keimyung University, 1095 Dalgubeol-daero, Daegu and S. Korea ; 2 Department of Electronic and Electrical Engineering, Keimyung University, 1095 Dalgubeol-daero, Daegu and S. Korea

ISBN: 978-989-758-350-6

Keyword(s): Data Mining, Smart Tire, Tire Built-in Sensor, Strain Gage, Tire Deformation Value, Vehicle Load.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Cognitive Systems ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Hybrid Intelligent Systems ; Model-Based Reasoning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: A smart tire is required to improve driving safety for an intelligent vehicle especially for automated driving electric vehicles. It is necessary to provide information of tire contact forces (vertical, longitudinal, and lateral directions) to control velocity and steering angle of the autonomous vehicle so as to ensure driving stability. This study presents a smart tire system with the data mining to estimate the vertical load by using the tire deformation data in particular. Firstly, the hardware system construction of the smart tire in which tire deformation on driving by using strain gauge is described. And then the test condition is set up and total 27 sets of experimental data are processed to perform correlation analysis for specifications of measured waves. Next, the estimation algorithm of smart tire vertical load is derived by considering the area of tire-ground contact patch and also by introducing compensate coefficient of transverse direction length of contact area. The e xperimental results show the proposed estimation algorithm is feasible and precise. The advanced adaptive and precise estimation algorithm with artificial neural network will be developed further. (More)

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Paper citation in several formats:
Lee, J.; Liu, H.; Seo, Y.; Kwak, S.; Lee, H.; Jo, H. and Park, S. (2019). An Investigation on the Data Mining to Develop Smart Tire.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 480-487. DOI: 10.5220/0007311104800487

@conference{icaart19,
author={Jae{-}Cheon Lee. and Hao Liu. and Young Gi Seo. and Seong Woo Kwak. and Ho Seung Lee. and Hae Jun Jo. and Sangsu Park.},
title={An Investigation on the Data Mining to Develop Smart Tire},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={480-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007311104800487},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - An Investigation on the Data Mining to Develop Smart Tire
SN - 978-989-758-350-6
AU - Lee, J.
AU - Liu, H.
AU - Seo, Y.
AU - Kwak, S.
AU - Lee, H.
AU - Jo, H.
AU - Park, S.
PY - 2019
SP - 480
EP - 487
DO - 10.5220/0007311104800487

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