research-article

On the limits of positioning-based pedestrian risk awareness

Authors:

Carlo Borgiattino,

Marco Gruteser,

Yingying ChenAuthors Info & Claims

MARS '14: Proceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems

Pages 23 - 28

https://doi.org/10.1145/2609829.2609834

Published: 11 June 2014 Publication History

Abstract

This paper studies the use of positioning techniques for sensing when pedestrians are at an increased risk of a traffic accident. Such sensing techniques could support augmented reality applications that increase pedestrian safety. We discuss requirements for pedestrian risk detection from rural to urban environments and consider algorithms relying on inertial and positioning sensors for distinguishing safe and unsafe walking locations. We study the limits of this approach through walking trials in different environments.

References

[1]

ito world. http://goo.gl/sWMVHT.

[2]

Openstreetmap. http://www.openstreetmap.org/.

[3]

Openstreetmap wiki. http://goo.gl/66Vlki.

[4]

U.S. Department of Transportation, DSRC. http://www.its.dot.gov/factsheets/dsrc_factsheet.htm.

[5]

OKI Electric Industry Co. DSRC attachment for mobile phones, January 2009.

[6]

UK Daily Mail. Texting while walking blamed in the nationwide increase of pedestrian deaths. http://goo.gl/kaKQgc.

[7]

K. David and A. Flach. Car-2-x and pedestrian safety. Vehicular Technology Magazine, 2010.

[8]

Andreas Fackelmeier, Christian Morhart, and Erwin Biebl. Dual frequency methods for identifying hidden targets in road traffic. In Advanced Microsystems for Automotive Applications. 2008.

[9]

A. Flach and K. David. Combining radio transmission with filters for pedestrian safety: Experiments and simulations. In Vehicular Technology Conference Fall, 2010.

[10]

A. Flach, A.Q. Memon, Sian Lun Lau, and K. David. Pedestrian movement recognition for radio based collision avoidance: A performance analysis. In Vehicular Technology Conference (VTC Spring), 2011.

[11]

Transportation for America. Dangerous by design, 2011.

[12]

T. Gandhi and M.M. Trivedi. Pedestrian protection systems: Issues, survey, and challenges. Intelligent Transportation Systems, IEEE Transactions on, 2007.

Digital Library

[13]

Boston Globe. Put that phone down and just walk. http://goo.gl/EwgrMg.

[14]

Joshua S Greenfeld. Matching gps observations to locations on a digital map. In National Research Council (US). Transportation Research Board. Meeting (81st: 2002: Washington, DC). Preprint CD-ROM, 2002.

[15]

Liberty Mutual Insurance. Study shows three out of five pedestrians prioritize smartphones over safety when crossing street, June 2013.

[16]

D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava. Sensloc: sensing everyday places and paths using less energy. In Embedded Networked Sensor Systems (SenSys), 2010.

Digital Library

[17]

New Jersey Department of Transportation. Roadway design manual.

[18]

U.S Department of Transportation. Traffic safety facts, August 2013.

[19]

C. Qin, X. Bao, R. Roy Choudhury, and S. Nelakuditi. Tagsense: a smartphone-based approach to automatic image tagging. In International Conference on Mobile Systems, Applications, and Services (MobiSys), 2011.

Digital Library

[20]

C. Sugimoto, Y. Nakamura, and T. Hashimoto. Prototype of pedestrian-to-vehicle communication system for the prevention of pedestrian accidents using both 3g wireless and wlan communication. In 3rd International Symposium on Wireless Pervasive Computing, 2008.

[21]

C. Voigtmann, Sian Lun Lau, and K. David. Evaluation of a collaborative-based filter technique to proactively detect pedestrians at risk. In Vehicular Technology Conference (VTC Fall), 2012.

[22]

Tianyu Wang, Giuseppe Cardone, Antonio Corradi, Lorenzo Torresani, and Andrew T. Campbell. Walksafe: a pedestrian safety app for mobile phone users who walk and talk while crossing roads. In Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, HotMobile '12.

Digital Library

[23]

Christopher E White, David Bernstein, and Alain L Kornhauser. Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies, 2000.

[24]

P. Zhou, Y. Zheng, Z. Li, M. Li, and G. Shen. Iodetector: A generic service for indoor outdoor detection. In Embedded Networked Sensor Systems (SenSys), 2012.

Digital Library

Cited By

Stefanidi HTatzgern MMeschtscherjakov A(2024)Augmented Reality on the Move: A Systematic Literature Review for Vulnerable Road UsersProceedings of the ACM on Human-Computer Interaction10.1145/36764908:MHCI(1-30)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676490
Nocera ACiattaglia GRaimondi MSenigagliesi LGambi E(2024)Identification of Smartphone Zombies and Normal Pedestrians Using FMCW Radar and Machine Learning2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444294(1-4)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444294
Shohel Parvez MMoridpour S(2024)Application of smart technologies in safety of vulnerable road users: A reviewInternational Journal of Transportation Science and Technology10.1016/j.ijtst.2024.07.006Online publication date: Jul-2024
https://doi.org/10.1016/j.ijtst.2024.07.006
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On the limits of positioning-based pedestrian risk awareness

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cover image ACM Conferences

MARS '14: Proceedings of the 2014 workshop on Mobile augmented reality and robotic technology-based systems

June 2014

60 pages

ISBN:9781450328234

DOI:10.1145/2609829

General Chair:
Mooi Choo Chuah
Lehigh University
,
Program Chair:
Pan Hui
HKUST

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 June 2014

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MobiSys'14

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SIGMOBILE

MobiSys'14: The 12th Annual International Conference on Mobile Systems, Applications, and Services

June 16, 2014

New Hampshire, Bretton Woods, USA

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MARS '14 Paper Acceptance Rate 6 of 7 submissions, 86%;

Overall Acceptance Rate 6 of 7 submissions, 86%

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Cited By

Stefanidi HTatzgern MMeschtscherjakov A(2024)Augmented Reality on the Move: A Systematic Literature Review for Vulnerable Road UsersProceedings of the ACM on Human-Computer Interaction10.1145/36764908:MHCI(1-30)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676490
Nocera ACiattaglia GRaimondi MSenigagliesi LGambi E(2024)Identification of Smartphone Zombies and Normal Pedestrians Using FMCW Radar and Machine Learning2024 IEEE International Conference on Consumer Electronics (ICCE)10.1109/ICCE59016.2024.10444294(1-4)Online publication date: 6-Jan-2024
https://doi.org/10.1109/ICCE59016.2024.10444294
Shohel Parvez MMoridpour S(2024)Application of smart technologies in safety of vulnerable road users: A reviewInternational Journal of Transportation Science and Technology10.1016/j.ijtst.2024.07.006Online publication date: Jul-2024
https://doi.org/10.1016/j.ijtst.2024.07.006
Nourbakhshrezaei AJadidi MSohn G(2023)Improving Cyclists’ Safety Using Intelligent Situational Awareness SystemSustainability10.3390/su1504286615:4(2866)Online publication date: 4-Feb-2023
https://doi.org/10.3390/su15042866
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Chavhan SKumar SGupta DAlkhayyat AKhanna AManikandan R(2023)Edge-Empowered Communication-Based Vehicle and Pedestrian Trajectory Perception System for Smart CitiesIEEE Internet of Things Journal10.1109/JIOT.2023.325464710:21(18951-18960)Online publication date: 1-Nov-2023
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Morold MBachmann MDavid K(2022)Toward Context Awareness for Cooperative Vulnerable Road User Collision Avoidance: Incorporating Related Contextual InformationIEEE Vehicular Technology Magazine10.1109/MVT.2022.317307517:3(75-83)Online publication date: Sep-2022
https://doi.org/10.1109/MVT.2022.3173075
Kabil ARabieh KKaleem FAzer M(2022)Vehicle to Pedestrian Systems: Survey, Challenges and Recent TrendsIEEE Access10.1109/ACCESS.2022.322477210(123981-123994)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3224772
Colley MLi SRukzio E(2021)Increasing Pedestrian Safety Using External Communication of Autonomous Vehicles for Signalling HazardsProceedings of the 23rd International Conference on Mobile Human-Computer Interaction10.1145/3447526.3472024(1-10)Online publication date: 27-Sep-2021
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Vourgidis IMaglaras LAlfakeeh AAl-Bayatti AFerrag M(2020)Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport SystemsSensors10.3390/s2004099720:4(997)Online publication date: 13-Feb-2020
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