A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)
83 Downloads (Pure)

Abstract

This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.
Original languageEnglish
Title of host publicationUbiComp & ISWC'15
Subtitle of host publicationproceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1169-1177
Number of pages9
ISBN (Print)978-1-4503-3575-1
DOIs
Publication statusPublished - Sep 2015
Event ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers - Osaka, Japan
Duration: 7 Sep 201511 Sep 2015

Conference

Conference ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers
Abbreviated titleUbiComp & ISWC
CountryJapan
CityOsaka
Period7/09/1511/09/15

Fingerprint

Smartphones
Gyroscopes
Clustering algorithms
Pavements
Error analysis
Learning systems
Sensors

Keywords

  • EWI-26651
  • Smartphone Sensing
  • Driver Behaviour
  • METIS-315138
  • IR-98920
  • Anomaly Detection
  • Road Monitoring

Cite this

Seraj, F., Zhang, K., Türkes, O., Meratnia, N., & Havinga, P. J. M. (2015). A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. In UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 1169-1177). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2800835.2800981
Seraj, Fatjon ; Zhang, Kui ; Türkes, Okan ; Meratnia, Nirvana ; Havinga, Paul J.M. / A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers. New York : Association for Computing Machinery (ACM), 2015. pp. 1169-1177
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title = "A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior",
abstract = "This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70{\%} of the swerves and 95{\%} of the turns on the road.",
keywords = "EWI-26651, Smartphone Sensing, Driver Behaviour, METIS-315138, IR-98920, Anomaly Detection, Road Monitoring",
author = "Fatjon Seraj and Kui Zhang and Okan T{\"u}rkes and Nirvana Meratnia and Havinga, {Paul J.M.}",
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doi = "10.1145/2800835.2800981",
language = "English",
isbn = "978-1-4503-3575-1",
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}

Seraj, F, Zhang, K, Türkes, O, Meratnia, N & Havinga, PJM 2015, A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. in UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers. Association for Computing Machinery (ACM), New York, pp. 1169-1177, ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2015 ACM International Symposium on Wearable Computers, Osaka, Japan, 7/09/15. https://doi.org/10.1145/2800835.2800981

A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. / Seraj, Fatjon; Zhang, Kui; Türkes, Okan; Meratnia, Nirvana; Havinga, Paul J.M.

UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers. New York : Association for Computing Machinery (ACM), 2015. p. 1169-1177.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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T1 - A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior

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N2 - This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.

AB - This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.

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KW - Smartphone Sensing

KW - Driver Behaviour

KW - METIS-315138

KW - IR-98920

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Seraj F, Zhang K, Türkes O, Meratnia N, Havinga PJM. A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. In UbiComp & ISWC'15: proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the proceedings of the 2015 ACM International Symposium on Wearable Computers. New York: Association for Computing Machinery (ACM). 2015. p. 1169-1177 https://doi.org/10.1145/2800835.2800981