Comfort and Time Efficiency: A Roundabout Case Study

Yanggu  Zheng; Barys Shyrokau; Tamas Keviczky

doi:10.1109/ITSC48978.2021.9564921

Comfort and Time Efficiency: A Roundabout Case Study

Yanggu Zheng, Barys Shyrokau, Tamas Keviczky

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

28 Downloads (Pure)

Abstract

The public acceptance of automated driving is influenced by multiple factors. Apart from safety being of top priority, comfort and time efficiency also have an impact on the popularity of automated vehicles. These two factors contradict each other as optimizing for one results in the degradation of the other. We investigate in this paper how such a multiobjective problem is approached by human drivers and by numerical optimization in the roundabout scenario, which is compact in size but complex to handle. The human drivers' behavior is first observed using naturalistic driving data. The average trajectories and distribution of peak accelerations were extracted after model-based fitting and removal of erroneous samples. The processed data is shared online as an open-access dataset. Then, an optimization problem is formulated and solved to find the numerically optimal motion profile in terms of comfort and time efficiency. The weighted sum of travel time and discomfort is minimized. By adjusting the weight distribution, we present different motion profiles favoring optimal comfort, human-like acceleration magnitudes, and agility, respectively.

Original language	English
Title of host publication	Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Publisher	IEEE
Pages	3877-3883
ISBN (Electronic)	978-1-7281-9142-3
ISBN (Print)	978-1-7281-9143-0
DOIs	https://doi.org/10.1109/ITSC48978.2021.9564921
Publication status	Published - 2021
Event	ITSC 2021: 24th IEEE International Intelligent Transportation Systems Conference - Virtual at Indianapolis, United States Duration: 19 Sept 2021 → 22 Sept 2021 Conference number: 24th

Conference

Conference	ITSC 2021
Country/Territory	United States
City	Virtual at Indianapolis
Period	19/09/21 → 22/09/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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@inproceedings{bab66da0e5f140c7a189e3dc19990d8c,

title = "Comfort and Time Efficiency: A Roundabout Case Study",

abstract = "The public acceptance of automated driving is influenced by multiple factors. Apart from safety being of top priority, comfort and time efficiency also have an impact on the popularity of automated vehicles. These two factors contradict each other as optimizing for one results in the degradation of the other. We investigate in this paper how such a multiobjective problem is approached by human drivers and by numerical optimization in the roundabout scenario, which is compact in size but complex to handle. The human drivers' behavior is first observed using naturalistic driving data. The average trajectories and distribution of peak accelerations were extracted after model-based fitting and removal of erroneous samples. The processed data is shared online as an open-access dataset. Then, an optimization problem is formulated and solved to find the numerically optimal motion profile in terms of comfort and time efficiency. The weighted sum of travel time and discomfort is minimized. By adjusting the weight distribution, we present different motion profiles favoring optimal comfort, human-like acceleration magnitudes, and agility, respectively.",

author = "Yanggu Zheng and Barys Shyrokau and Tamas Keviczky",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; ITSC 2021 : 24th IEEE International Intelligent Transportation Systems Conference ; Conference date: 19-09-2021 Through 22-09-2021",

year = "2021",

doi = "10.1109/ITSC48978.2021.9564921",

language = "English",

isbn = "978-1-7281-9143-0",

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booktitle = "Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)",

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address = "United States",

}

TY - GEN

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T2 - ITSC 2021

AU - Zheng, Yanggu

AU - Shyrokau, Barys

AU - Keviczky, Tamas

N1 - Conference code: 24th

PY - 2021

Y1 - 2021

N2 - The public acceptance of automated driving is influenced by multiple factors. Apart from safety being of top priority, comfort and time efficiency also have an impact on the popularity of automated vehicles. These two factors contradict each other as optimizing for one results in the degradation of the other. We investigate in this paper how such a multiobjective problem is approached by human drivers and by numerical optimization in the roundabout scenario, which is compact in size but complex to handle. The human drivers' behavior is first observed using naturalistic driving data. The average trajectories and distribution of peak accelerations were extracted after model-based fitting and removal of erroneous samples. The processed data is shared online as an open-access dataset. Then, an optimization problem is formulated and solved to find the numerically optimal motion profile in terms of comfort and time efficiency. The weighted sum of travel time and discomfort is minimized. By adjusting the weight distribution, we present different motion profiles favoring optimal comfort, human-like acceleration magnitudes, and agility, respectively.

AB - The public acceptance of automated driving is influenced by multiple factors. Apart from safety being of top priority, comfort and time efficiency also have an impact on the popularity of automated vehicles. These two factors contradict each other as optimizing for one results in the degradation of the other. We investigate in this paper how such a multiobjective problem is approached by human drivers and by numerical optimization in the roundabout scenario, which is compact in size but complex to handle. The human drivers' behavior is first observed using naturalistic driving data. The average trajectories and distribution of peak accelerations were extracted after model-based fitting and removal of erroneous samples. The processed data is shared online as an open-access dataset. Then, an optimization problem is formulated and solved to find the numerically optimal motion profile in terms of comfort and time efficiency. The weighted sum of travel time and discomfort is minimized. By adjusting the weight distribution, we present different motion profiles favoring optimal comfort, human-like acceleration magnitudes, and agility, respectively.

UR - http://www.scopus.com/inward/record.url?scp=85118448988&partnerID=8YFLogxK

U2 - 10.1109/ITSC48978.2021.9564921

DO - 10.1109/ITSC48978.2021.9564921

M3 - Conference contribution

SN - 978-1-7281-9143-0

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EP - 3883

BT - Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

PB - IEEE

Y2 - 19 September 2021 through 22 September 2021

ER -

Comfort and Time Efficiency: A Roundabout Case Study

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