Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm

Md Abdul Latif Sarker; Dong Seog Han

doi:10.1109/APCC55198.2022.9943704

Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm

Md Abdul Latif Sarker, Dong Seog Han

School of Electronics Engineering

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

2 Scopus citations

Abstract

This paper presents a human-centric autonomous driving system, which is based on a two-stage machine learning algorithm. In particular, driving perception and human features are integrated to develop human-centric autonomous vehicles. Hence, we propose two-stage machine learning algorithms to identify the driver features such as age, location, sense, etc. We consider both online and offline learning to construct a two-stage distribution model and determine the relationship between the driver features and its cluster. The simulation results show the performance of the proposed two-stage learning algorithms in terms of a driver's feature training performance.

Original language	English
Title of host publication	APCC 2022 - 27th Asia-Pacific Conference on Communications
Subtitle of host publication	Creating Innovative Communication Technologies for Post-Pandemic Era
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	334-335
Number of pages	2
ISBN (Electronic)	9781665499279
DOIs	https://doi.org/10.1109/APCC55198.2022.9943704
State	Published - 2022
Event	27th Asia-Pacific Conference on Communications, APCC 2022 - Jeju Island, Korea, Republic of Duration: 19 Oct 2022 → 21 Oct 2022

Publication series

Name	APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era

Conference

Conference	27th Asia-Pacific Conference on Communications, APCC 2022
Country/Territory	Korea, Republic of
City	Jeju Island
Period	19/10/22 → 21/10/22

Keywords

autonomous driving
Human-centric
machine learning
online and offline learning

Access to Document

10.1109/APCC55198.2022.9943704

Cite this

Latif Sarker, M. A., & Seog Han, D. (2022). Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm. In APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era (pp. 334-335). (APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APCC55198.2022.9943704

Latif Sarker, Md Abdul ; Seog Han, Dong. / Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm. APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 334-335 (APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era).

@inproceedings{5b09f670afa44d75a98133ae2416aff8,

title = "Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm",

abstract = "This paper presents a human-centric autonomous driving system, which is based on a two-stage machine learning algorithm. In particular, driving perception and human features are integrated to develop human-centric autonomous vehicles. Hence, we propose two-stage machine learning algorithms to identify the driver features such as age, location, sense, etc. We consider both online and offline learning to construct a two-stage distribution model and determine the relationship between the driver features and its cluster. The simulation results show the performance of the proposed two-stage learning algorithms in terms of a driver's feature training performance.",

keywords = "autonomous driving, Human-centric, machine learning, online and offline learning",

author = "{Latif Sarker}, {Md Abdul} and {Seog Han}, Dong",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th Asia-Pacific Conference on Communications, APCC 2022 ; Conference date: 19-10-2022 Through 21-10-2022",

year = "2022",

doi = "10.1109/APCC55198.2022.9943704",

language = "English",

series = "APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "334--335",

booktitle = "APCC 2022 - 27th Asia-Pacific Conference on Communications",

address = "United States",

}

Latif Sarker, MA & Seog Han, D 2022, Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm. in APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era. APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era, Institute of Electrical and Electronics Engineers Inc., pp. 334-335, 27th Asia-Pacific Conference on Communications, APCC 2022, Jeju Island, Korea, Republic of, 19/10/22. https://doi.org/10.1109/APCC55198.2022.9943704

Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm. / Latif Sarker, Md Abdul; Seog Han, Dong.
APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era. Institute of Electrical and Electronics Engineers Inc., 2022. p. 334-335 (APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era).

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

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AU - Latif Sarker, Md Abdul

AU - Seog Han, Dong

PY - 2022

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N2 - This paper presents a human-centric autonomous driving system, which is based on a two-stage machine learning algorithm. In particular, driving perception and human features are integrated to develop human-centric autonomous vehicles. Hence, we propose two-stage machine learning algorithms to identify the driver features such as age, location, sense, etc. We consider both online and offline learning to construct a two-stage distribution model and determine the relationship between the driver features and its cluster. The simulation results show the performance of the proposed two-stage learning algorithms in terms of a driver's feature training performance.

AB - This paper presents a human-centric autonomous driving system, which is based on a two-stage machine learning algorithm. In particular, driving perception and human features are integrated to develop human-centric autonomous vehicles. Hence, we propose two-stage machine learning algorithms to identify the driver features such as age, location, sense, etc. We consider both online and offline learning to construct a two-stage distribution model and determine the relationship between the driver features and its cluster. The simulation results show the performance of the proposed two-stage learning algorithms in terms of a driver's feature training performance.

KW - autonomous driving

KW - Human-centric

KW - machine learning

KW - online and offline learning

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DO - 10.1109/APCC55198.2022.9943704

M3 - Conference contribution

AN - SCOPUS:85143085463

T3 - APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era

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PB - Institute of Electrical and Electronics Engineers Inc.

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Latif Sarker MA, Seog Han D. Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm. In APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era. Institute of Electrical and Electronics Engineers Inc. 2022. p. 334-335. (APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era). doi: 10.1109/APCC55198.2022.9943704

Human-Centric Autonomous Driving Based on a Two-Stage Machine Learning Algorithm

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Keywords

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