AutoCoach: Driving behavior management using intelligent IoT services

Zahraa Marafie; Kwei Jay Lin; Daben Wang; Haoyu Lyu; Yu Meng; Takayuki Ito

doi:10.1109/SOCA.2019.00023

AutoCoach: Driving behavior management using intelligent IoT services

Zahraa Marafie, Kwei Jay Lin, Daben Wang, Haoyu Lyu, Yu Meng, Takayuki Ito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

AutoCoach is an intelligent agent intended for improving automobile drivers' performance by applying persuasive technology. System models like Advanced driver-assistance (ADAS) and some Usage-based-Insurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes drivers' personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavior's risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible time-period. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	103-110
Number of pages	8
ISBN (Electronic)	9781728154114
DOIs	https://doi.org/10.1109/SOCA.2019.00023
State	Published - 11 2019
Externally published	Yes
Event	12th IEEE Conference on Service-Oriented Computing and Applications, SOCA 2019 - Kaohsiung, Taiwan Duration: 18 11 2019 → 21 11 2019

Publication series

Name	Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019

Conference

Conference	12th IEEE Conference on Service-Oriented Computing and Applications, SOCA 2019
Country/Territory	Taiwan
City	Kaohsiung
Period	18/11/19 → 21/11/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Driving Behavior Detection
Driving Behavior Management
Feedback
Intelligent Agent
IoT
Personalization

Access to Document

10.1109/SOCA.2019.00023

Cite this

Marafie, Z., Lin, K. J., Wang, D., Lyu, H., Meng, Y., & Ito, T. (2019). AutoCoach: Driving behavior management using intelligent IoT services. In Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019 (pp. 103-110). Article 8953021 (Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SOCA.2019.00023

Marafie, Zahraa ; Lin, Kwei Jay ; Wang, Daben et al. / AutoCoach : Driving behavior management using intelligent IoT services. Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 103-110 (Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019).

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title = "AutoCoach: Driving behavior management using intelligent IoT services",

abstract = "AutoCoach is an intelligent agent intended for improving automobile drivers' performance by applying persuasive technology. System models like Advanced driver-assistance (ADAS) and some Usage-based-Insurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes drivers' personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavior's risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible time-period. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.",

keywords = "Driving Behavior Detection, Driving Behavior Management, Feedback, Intelligent Agent, IoT, Personalization",

author = "Zahraa Marafie and Lin, {Kwei Jay} and Daben Wang and Haoyu Lyu and Yu Meng and Takayuki Ito",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th IEEE Conference on Service-Oriented Computing and Applications, SOCA 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/SOCA.2019.00023",

language = "英语",

series = "Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019",

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Marafie, Z, Lin, KJ, Wang, D, Lyu, H, Meng, Y & Ito, T 2019, AutoCoach: Driving behavior management using intelligent IoT services. in Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019., 8953021, Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019, Institute of Electrical and Electronics Engineers Inc., pp. 103-110, 12th IEEE Conference on Service-Oriented Computing and Applications, SOCA 2019, Kaohsiung, Taiwan, 18/11/19. https://doi.org/10.1109/SOCA.2019.00023

AutoCoach: Driving behavior management using intelligent IoT services. / Marafie, Zahraa; Lin, Kwei Jay; Wang, Daben et al.
Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 103-110 8953021 (Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Marafie, Zahraa

AU - Lin, Kwei Jay

AU - Wang, Daben

AU - Lyu, Haoyu

AU - Meng, Yu

AU - Ito, Takayuki

PY - 2019/11

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AB - AutoCoach is an intelligent agent intended for improving automobile drivers' performance by applying persuasive technology. System models like Advanced driver-assistance (ADAS) and some Usage-based-Insurance (UBI) share an aim to increase car and road safety. However, most prior models do not consider the differences between driving habits. The AutoCoach design includes two unique components to build an effective persuasive system. The first component is the personality classification, which recognizes drivers' personalities by analyzing driving behavior patterns. The second component is the rewarding system, which determines the current driving behavior's risk score based on some immediate past behavior. We propose the idea of memory factor, which decides when to provide feedback to drivers based on their personality. This memory factor identifies the most critical behaviors within a flexible time-period. AutoCoach then decides on feedback to maintain safe driving or improve the level of awareness for risky driving habits.

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 18 November 2019 through 21 November 2019

ER -

Marafie Z, Lin KJ, Wang D, Lyu H, Meng Y, Ito T. AutoCoach: Driving behavior management using intelligent IoT services. In Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 103-110. 8953021. (Proceedings - 2019 IEEE 12th Conference on Service-Oriented Computing and Applications, SOCA 2019). doi: 10.1109/SOCA.2019.00023

AutoCoach: Driving behavior management using intelligent IoT services

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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