CrowdParking: Crowdsourcing based parking navigation in autonomous driving era

Chao Zhu; Abbas Mehrabi; Yu Xiao; Yinghong Wen

doi:10.1109/ICEAA.2019.8879201

CrowdParking: Crowdsourcing based parking navigation in autonomous driving era

Chao Zhu, Abbas Mehrabi, Yu Xiao, Yinghong Wen

Beijing Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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Abstract

Finding a free road side parking in urban area is considered as one of the most challenging driving tasks, especially for the autonomous vehicles with limited sight (e.g. short range sensing) and brain (compared with human beings). To assist autonomous vehicle parking in urban area, we propose a novel parking scheme CrowdParking, which applies crowdsourcing and vehicular fog computing to collect parking information from vehicles, locate free parking spaces from crowdsourced data. We also explore the variation of parking availability from a real world data set and find that the availability of specific parking lot has certain relationship with the traffic condition of nearby roads. Based on the observations, we propose the vision of estimating the parking availability with taking into account the traffic condition in neighborhood.

Original language	English
Title of host publication	Proceedings of the 2019 21st International Conference on Electromagnetics in Advanced Applications, ICEAA 2019
Publisher	IEEE
Pages	1401-1405
Number of pages	5
ISBN (Electronic)	9781728105635
DOIs	https://doi.org/10.1109/ICEAA.2019.8879201
Publication status	Published - 1 Sept 2019
MoE publication type	A4 Conference publication
Event	International Conference on Electromagnetics in Advanced Applications - Granada, Spain Duration: 9 Sept 2019 → 13 Sept 2019 Conference number: 21

Conference

Conference	International Conference on Electromagnetics in Advanced Applications
Abbreviated title	ICEAA
Country/Territory	Spain
City	Granada
Period	09/09/2019 → 13/09/2019

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10.1109/ICEAA.2019.8879201

SCI_Zhu_et.al_CrowdParkingAccepted author manuscript, 7.31 MB

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@inproceedings{940120d40f6c41dcb43b0feb3ec2f29d,

title = "CrowdParking: Crowdsourcing based parking navigation in autonomous driving era",

abstract = "Finding a free road side parking in urban area is considered as one of the most challenging driving tasks, especially for the autonomous vehicles with limited sight (e.g. short range sensing) and brain (compared with human beings). To assist autonomous vehicle parking in urban area, we propose a novel parking scheme CrowdParking, which applies crowdsourcing and vehicular fog computing to collect parking information from vehicles, locate free parking spaces from crowdsourced data. We also explore the variation of parking availability from a real world data set and find that the availability of specific parking lot has certain relationship with the traffic condition of nearby roads. Based on the observations, we propose the vision of estimating the parking availability with taking into account the traffic condition in neighborhood.",

author = "Chao Zhu and Abbas Mehrabi and Yu Xiao and Yinghong Wen",

year = "2019",

month = sep,

day = "1",

doi = "10.1109/ICEAA.2019.8879201",

language = "English",

pages = "1401--1405",

booktitle = "Proceedings of the 2019 21st International Conference on Electromagnetics in Advanced Applications, ICEAA 2019",

publisher = "IEEE",

address = "United States",

note = "International Conference on Electromagnetics in Advanced Applications, ICEAA ; Conference date: 09-09-2019 Through 13-09-2019",

}

Zhu, C, Mehrabi, A, Xiao, Y & Wen, Y 2019, CrowdParking: Crowdsourcing based parking navigation in autonomous driving era. in Proceedings of the 2019 21st International Conference on Electromagnetics in Advanced Applications, ICEAA 2019., 8879201, IEEE, pp. 1401-1405, International Conference on Electromagnetics in Advanced Applications, Granada, Spain, 09/09/2019. https://doi.org/10.1109/ICEAA.2019.8879201

CrowdParking: Crowdsourcing based parking navigation in autonomous driving era. / Zhu, Chao; Mehrabi, Abbas; Xiao, Yu et al.
Proceedings of the 2019 21st International Conference on Electromagnetics in Advanced Applications, ICEAA 2019. IEEE, 2019. p. 1401-1405 8879201.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - CrowdParking: Crowdsourcing based parking navigation in autonomous driving era

AU - Zhu, Chao

AU - Mehrabi, Abbas

AU - Xiao, Yu

AU - Wen, Yinghong

N1 - Conference code: 21

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Finding a free road side parking in urban area is considered as one of the most challenging driving tasks, especially for the autonomous vehicles with limited sight (e.g. short range sensing) and brain (compared with human beings). To assist autonomous vehicle parking in urban area, we propose a novel parking scheme CrowdParking, which applies crowdsourcing and vehicular fog computing to collect parking information from vehicles, locate free parking spaces from crowdsourced data. We also explore the variation of parking availability from a real world data set and find that the availability of specific parking lot has certain relationship with the traffic condition of nearby roads. Based on the observations, we propose the vision of estimating the parking availability with taking into account the traffic condition in neighborhood.

AB - Finding a free road side parking in urban area is considered as one of the most challenging driving tasks, especially for the autonomous vehicles with limited sight (e.g. short range sensing) and brain (compared with human beings). To assist autonomous vehicle parking in urban area, we propose a novel parking scheme CrowdParking, which applies crowdsourcing and vehicular fog computing to collect parking information from vehicles, locate free parking spaces from crowdsourced data. We also explore the variation of parking availability from a real world data set and find that the availability of specific parking lot has certain relationship with the traffic condition of nearby roads. Based on the observations, we propose the vision of estimating the parking availability with taking into account the traffic condition in neighborhood.

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DO - 10.1109/ICEAA.2019.8879201

M3 - Conference article in proceedings

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EP - 1405

BT - Proceedings of the 2019 21st International Conference on Electromagnetics in Advanced Applications, ICEAA 2019

PB - IEEE

T2 - International Conference on Electromagnetics in Advanced Applications

Y2 - 9 September 2019 through 13 September 2019

ER -

CrowdParking: Crowdsourcing based parking navigation in autonomous driving era

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DataFog: A Data-Driven Platform for Capacity and Resource Management in Vehicular Fog Computing

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CrowdParking: Crowdsourcing based parking navigation in autonomous driving era

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DataFog: A Data-Driven Platform for Capacity and Resource Management in Vehicular Fog Computing

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