Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

Ali Alizadeh; Majid Moghadam; Yunus Bicer; Nazim Kemal Ure; Ugur Yavas; Can Kurtulus

doi:10.1109/ITSC.2019.8917192

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

Ali Alizadeh, Majid Moghadam, Yunus Bicer, Nazim Kemal Ure, Ugur Yavas, Can Kurtulus

Yapay Zeka ve Veri Mühendisliği Bölümü

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

89 Atıf (Scopus)

Özet

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1399-1404
Sayfa sayısı	6
ISBN (Elektronik)	9781538670248
DOI'lar	https://doi.org/10.1109/ITSC.2019.8917192
Yayın durumu	Yayınlandı - Eki 2019
Etkinlik	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Süre: 27 Eki 2019 → 30 Eki 2019

Yayın serisi

Adı	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Ülke/Bölge	New Zealand
Şehir	Auckland
Periyot	27/10/19 → 30/10/19

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Belgeye Erişim

10.1109/ITSC.2019.8917192

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Alizadeh, A., Moghadam, M., Bicer, Y., Ure, N. K., Yavas, U., & Kurtulus, C. (2019). Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 1399-1404). Makale 8917192 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917192

Alizadeh, Ali ; Moghadam, Majid ; Bicer, Yunus et al. / Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1399-1404 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

@inproceedings{1e08acaeec094717aaa16a5e17735136,

title = "Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment",

abstract = "Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.",

author = "Ali Alizadeh and Majid Moghadam and Yunus Bicer and Ure, {Nazim Kemal} and Ugur Yavas and Can Kurtulus",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ITSC.2019.8917192",

language = "English",

series = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

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

pages = "1399--1404",

booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

address = "United States",

}

Alizadeh, A, Moghadam, M, Bicer, Y, Ure, NK, Yavas, U & Kurtulus, C 2019, Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8917192, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1399-1404, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, New Zealand, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917192

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. / Alizadeh, Ali; Moghadam, Majid; Bicer, Yunus et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1399-1404 8917192 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

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T1 - Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

AU - Alizadeh, Ali

AU - Moghadam, Majid

AU - Bicer, Yunus

AU - Ure, Nazim Kemal

AU - Yavas, Ugur

AU - Kurtulus, Can

PY - 2019/10

Y1 - 2019/10

N2 - Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

AB - Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.

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U2 - 10.1109/ITSC.2019.8917192

DO - 10.1109/ITSC.2019.8917192

M3 - Conference contribution

AN - SCOPUS:85076813552

T3 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

SP - 1399

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BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

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ER -

Alizadeh A, Moghadam M, Bicer Y, Ure NK, Yavas U, Kurtulus C. Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1399-1404. 8917192. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). doi: 10.1109/ITSC.2019.8917192

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

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