Dataset: An Indoor Smart Traffic Dataset and Data Collection System

Neiwen Ling; Yuze He; Nan Guan; Heming Fu; Guoliang Xing

doi:10.1145/3560905.3567772

Dataset: An Indoor Smart Traffic Dataset and Data Collection System

Neiwen Ling, Yuze He, Nan Guan, Heming Fu, Guoliang Xing^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

Smart traffic is an emerging research area gaining more attention due to a class of emerging applications such as autonomous driving. Most smart traffic scenarios are outdoors, which are hard to collect traffic data and build demanding sensing systems. In this work, an indoor smart traffic testbed with an F1TENTH autonomous driving vehicle is built, allowing the collection of traffic datasets under different scenarios and performing various smart traffic tasks. This novel data collection system and collected dataset can help research teams build various smart traffic systems and evaluate indoor smart traffic datasets. The collected traffic light dataset is publicly available at the link. © 2022 ACM.

Original language	English
Title of host publication	SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
Publisher	Association for Computing Machinery
Pages	1007-1010
ISBN (Print)	9781450398862
DOIs	https://doi.org/10.1145/3560905.3567772
Publication status	Published - Nov 2022
Event	20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022) - Hynes Convention Center, Boston, United States Duration: 6 Nov 2022 → 9 Nov 2022 https://sensys.acm.org/2022/

Publication series

Name	SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems

Conference

Conference	20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022)
Place	United States
City	Boston
Period	6/11/22 → 9/11/22
Internet address	https://sensys.acm.org/2022/

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Research Keywords

autonomous driving
dataset
F1TENTH
smart traffic

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10.1145/3560905.3567772

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Ling, N., He, Y., Guan, N., Fu, H., & Xing, G. (2022). Dataset: An Indoor Smart Traffic Dataset and Data Collection System. In SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (pp. 1007-1010). (SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems). Association for Computing Machinery. https://doi.org/10.1145/3560905.3567772

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title = "Dataset: An Indoor Smart Traffic Dataset and Data Collection System",

abstract = "Smart traffic is an emerging research area gaining more attention due to a class of emerging applications such as autonomous driving. Most smart traffic scenarios are outdoors, which are hard to collect traffic data and build demanding sensing systems. In this work, an indoor smart traffic testbed with an F1TENTH autonomous driving vehicle is built, allowing the collection of traffic datasets under different scenarios and performing various smart traffic tasks. This novel data collection system and collected dataset can help research teams build various smart traffic systems and evaluate indoor smart traffic datasets. The collected traffic light dataset is publicly available at the link. {\textcopyright} 2022 ACM.",

keywords = "autonomous driving, dataset, F1TENTH, smart traffic",

author = "Neiwen Ling and Yuze He and Nan Guan and Heming Fu and Guoliang Xing",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022) ; Conference date: 06-11-2022 Through 09-11-2022",

year = "2022",

month = nov,

doi = "10.1145/3560905.3567772",

language = "English",

isbn = "9781450398862",

series = "SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems",

publisher = "Association for Computing Machinery",

pages = "1007--1010",

booktitle = "SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems",

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url = "https://sensys.acm.org/2022/",

}

Ling, N, He, Y, Guan, N, Fu, H & Xing, G 2022, Dataset: An Indoor Smart Traffic Dataset and Data Collection System. in SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems, Association for Computing Machinery, pp. 1007-1010, 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022), Boston, United States, 6/11/22. https://doi.org/10.1145/3560905.3567772

Dataset: An Indoor Smart Traffic Dataset and Data Collection System. / Ling, Neiwen; He, Yuze; Guan, Nan et al.
SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, 2022. p. 1007-1010 (SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Dataset

T2 - 20th ACM Conference on Embedded Networked Sensor Systems (SenSys 2022)

AU - Ling, Neiwen

AU - He, Yuze

AU - Guan, Nan

AU - Fu, Heming

AU - Xing, Guoliang

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2022/11

Y1 - 2022/11

N2 - Smart traffic is an emerging research area gaining more attention due to a class of emerging applications such as autonomous driving. Most smart traffic scenarios are outdoors, which are hard to collect traffic data and build demanding sensing systems. In this work, an indoor smart traffic testbed with an F1TENTH autonomous driving vehicle is built, allowing the collection of traffic datasets under different scenarios and performing various smart traffic tasks. This novel data collection system and collected dataset can help research teams build various smart traffic systems and evaluate indoor smart traffic datasets. The collected traffic light dataset is publicly available at the link. © 2022 ACM.

AB - Smart traffic is an emerging research area gaining more attention due to a class of emerging applications such as autonomous driving. Most smart traffic scenarios are outdoors, which are hard to collect traffic data and build demanding sensing systems. In this work, an indoor smart traffic testbed with an F1TENTH autonomous driving vehicle is built, allowing the collection of traffic datasets under different scenarios and performing various smart traffic tasks. This novel data collection system and collected dataset can help research teams build various smart traffic systems and evaluate indoor smart traffic datasets. The collected traffic light dataset is publicly available at the link. © 2022 ACM.

KW - autonomous driving

KW - dataset

KW - F1TENTH

KW - smart traffic

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781450398862

T3 - SenSys - Proceedings of the ACM Conference on Embedded Networked Sensor Systems

SP - 1007

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BT - SenSys 2022 - Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems

PB - Association for Computing Machinery

Y2 - 6 November 2022 through 9 November 2022

ER -

Dataset: An Indoor Smart Traffic Dataset and Data Collection System

Abstract

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Bibliographical note

Research Keywords

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