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Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues

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Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues. / Zhang, Xu; Fang, Yuan; Cao, Yue et al.

In: IEEE Systems Journal, Vol. 16, No. 1, 31.03.2022, p. 351-361.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Zhang, X, Fang, Y, Cao, Y & Liu, S 2022, 'Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues', IEEE Systems Journal, vol. 16, no. 1, pp. 351-361. https://doi.org/10.1109/JSYST.2020.3036627

APA

Vancouver

Zhang X, Fang Y, Cao Y, Liu S. Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues. IEEE Systems Journal. 2022 Mar 31;16(1):351-361. Epub 2020 Nov 25. doi: 10.1109/JSYST.2020.3036627

Author

Zhang, Xu ; Fang, Yuan ; Cao, Yue et al. / Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues. In: IEEE Systems Journal. 2022 ; Vol. 16, No. 1. pp. 351-361.

Bibtex

@article{f6262c372aea48d5904ae22a21e0c87e,
title = "Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues",
abstract = "Advances in automotive industry as well as computing technology are making autonomous vehicle (AV) an appealing means of transportation. Vehicles are beyond the traditional source of commute, and leveled up to smart devices with computing capability. As one of the compelling features of AVs, the autonomous valet parking (AVP) allows for navigating and parking the car automatically without human interventions. Within this realm, long-range AVP (LAVP) extends auto-parking to a much larger scale compared to its short-range counterparts. It is worth noting that AV mobility is a pivotal concern with LAVP, involving dynamic patterns related to spatial-temporal features, such as varied parking and drop-off (or pick-up) demands with diverse customer journey planning. We herein target such critical decision-making on where to park and where to drop/pick-up upon customer requirements during their journeys. Concerning in practice that car parks are equipped with limited parking space, we thus introduce parking reservations and enable accurate estimations on future parking states. An efficient LAVP service framework enhanced with parking reservations is then proposed. Benefited from the intelligent predictions, parking load can be accurately predicted and greatly alleviated at individual car parks, thereby avoiding overcrowding effectively. Results show that significant performance gains can be achieved under the proposed scheme by comparing to other benchmarks, with respect to greatly reduced waiting duration for available parking space, as well as enhanced customer experiences in terms of reduced traveling period, etc. In particular, the number of parked vehicles across the network can be effectively balanced.",
keywords = "Autonomous valet parking (AVP), Autonomous vehicle (AV), Transportation planning",
author = "Xu Zhang and Yuan Fang and Yue Cao and Shuohan Liu",
note = "{\textcopyright}2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2022",
month = mar,
day = "31",
doi = "10.1109/JSYST.2020.3036627",
language = "English",
volume = "16",
pages = "351--361",
journal = "IEEE Systems Journal",
issn = "1932-8184",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Reservation Enhanced Autonomous Valet Parking Concerning Practicality Issues

AU - Zhang, Xu

AU - Fang, Yuan

AU - Cao, Yue

AU - Liu, Shuohan

N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/3/31

Y1 - 2022/3/31

N2 - Advances in automotive industry as well as computing technology are making autonomous vehicle (AV) an appealing means of transportation. Vehicles are beyond the traditional source of commute, and leveled up to smart devices with computing capability. As one of the compelling features of AVs, the autonomous valet parking (AVP) allows for navigating and parking the car automatically without human interventions. Within this realm, long-range AVP (LAVP) extends auto-parking to a much larger scale compared to its short-range counterparts. It is worth noting that AV mobility is a pivotal concern with LAVP, involving dynamic patterns related to spatial-temporal features, such as varied parking and drop-off (or pick-up) demands with diverse customer journey planning. We herein target such critical decision-making on where to park and where to drop/pick-up upon customer requirements during their journeys. Concerning in practice that car parks are equipped with limited parking space, we thus introduce parking reservations and enable accurate estimations on future parking states. An efficient LAVP service framework enhanced with parking reservations is then proposed. Benefited from the intelligent predictions, parking load can be accurately predicted and greatly alleviated at individual car parks, thereby avoiding overcrowding effectively. Results show that significant performance gains can be achieved under the proposed scheme by comparing to other benchmarks, with respect to greatly reduced waiting duration for available parking space, as well as enhanced customer experiences in terms of reduced traveling period, etc. In particular, the number of parked vehicles across the network can be effectively balanced.

AB - Advances in automotive industry as well as computing technology are making autonomous vehicle (AV) an appealing means of transportation. Vehicles are beyond the traditional source of commute, and leveled up to smart devices with computing capability. As one of the compelling features of AVs, the autonomous valet parking (AVP) allows for navigating and parking the car automatically without human interventions. Within this realm, long-range AVP (LAVP) extends auto-parking to a much larger scale compared to its short-range counterparts. It is worth noting that AV mobility is a pivotal concern with LAVP, involving dynamic patterns related to spatial-temporal features, such as varied parking and drop-off (or pick-up) demands with diverse customer journey planning. We herein target such critical decision-making on where to park and where to drop/pick-up upon customer requirements during their journeys. Concerning in practice that car parks are equipped with limited parking space, we thus introduce parking reservations and enable accurate estimations on future parking states. An efficient LAVP service framework enhanced with parking reservations is then proposed. Benefited from the intelligent predictions, parking load can be accurately predicted and greatly alleviated at individual car parks, thereby avoiding overcrowding effectively. Results show that significant performance gains can be achieved under the proposed scheme by comparing to other benchmarks, with respect to greatly reduced waiting duration for available parking space, as well as enhanced customer experiences in terms of reduced traveling period, etc. In particular, the number of parked vehicles across the network can be effectively balanced.

KW - Autonomous valet parking (AVP)

KW - Autonomous vehicle (AV)

KW - Transportation planning

U2 - 10.1109/JSYST.2020.3036627

DO - 10.1109/JSYST.2020.3036627

M3 - Journal article

VL - 16

SP - 351

EP - 361

JO - IEEE Systems Journal

JF - IEEE Systems Journal

SN - 1932-8184

IS - 1

ER -