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An Approach to Verify Heavy Vehicle Driver Fatigue Compliance Under Australian Chain of Responsibility Regulations

  • Son Anh Vo
  • Joel Scanlan
  • Luke Mirowski
  • Paul Turner
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)

Abstract

Heavy vehicle transportation is vital to the Australian logistics industry. However, it also experiences the highest number of work related accidents. Chain of responsibility regulations introduced by the National Heavy Vehicle Regulator (NHVR) extends obligations and liabilities for safety in heavy vehicle transport to all participants along with supply chains. As a result, finding mechanisms to support compliance with fatigue management rules has become important for the whole industry. The current compliance system is paper-based, and does not produce high quality compliance information and is proving to be expensive for supply chain participants to maintain. This paper presents an automated approach to verify heavy vehicle driver fatigue compliance. Drawing on data from a software tool (Logistics Fatigue Manager) developed by two of the authors, the automated approach deploys signature based detection techniques from Intrusion Detection Systems. The results highlight reduced costs, improved accuracy and speed of compliance verification.

Keywords

Fatigue compliance Heavy vehicle Intrusion Detection Systems Rule-based technique 

References

  1. 1.
    Department of Infrastructure and Regional Development-Australian Government. Freightline 1—Australia freight transport overview (2014)Google Scholar
  2. 2.
  3. 3.
    National Transport Insurance. Major Accident Investigation Report (2013)Google Scholar
  4. 4.
  5. 5.
  6. 6.
    Sirevaag, E.J., Stern, J.A.: Ocular measures of fatigue and cognitive factors. In: Engineering Psychophysiology: Issues and Applications, pp. 269–287 (2000)Google Scholar
  7. 7.
    Stern, J.A., Boyer, D., Schroeder, D.: Blink rate: a possible measure of fatigue. Hum. Factors 36, 285–297 (1994)CrossRefGoogle Scholar
  8. 8.
    Summala, H., Hakkanen, H., Mikkola, T., Sinkkonen, J.: Task effects on fatigue symptoms in overnight driving. Ergonomics 42, 798–806 (1999)CrossRefGoogle Scholar
  9. 9.
    Schleicher, R., Galley, N., Briest, S., Galley, L.: Blinks and saccades as indicators of fatigue in sleepiness warnings: looking tired? Ergonomics 51, 982–1010 (2008)CrossRefGoogle Scholar
  10. 10.
    Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R., Lopez, M.E.: Real-time system for monitoring driver vigilance. IEEE Trans. Intell. Transp. Syst. 7, 63–77 (2006)CrossRefGoogle Scholar
  11. 11.
    The No Nap. The NoNap Advantage (2017). http://www.thenonap.com/index.html
  12. 12.
    New South Wales Government. Operational Pilot of Electronic Work Diaries and Speed Monitoring Systems (2013)Google Scholar
  13. 13.
    Beigh, B.M.: A new classification scheme for intrusion detection systems. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 6, 56 (2014)Google Scholar
  14. 14.
    Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutor. 16, 303–336 (2014)CrossRefGoogle Scholar
  15. 15.
    Gogoi, P., Bhattacharyya, D., Borah, B., Kalita, J.K.: A survey of outlier detection methods in network anomaly identification. Comput. J. 54, 570–588 (2011)CrossRefGoogle Scholar
  16. 16.
    McHugh, J., Christie, A., Allen, J.: Defending yourself: the role of intrusion detection systems. IEEE Softw. 17, 42–51 (2000)CrossRefGoogle Scholar
  17. 17.
    Mokarian, A., Faraahi, A., Delavar, A.G.: False positives reduction techniques in intrusion detection systems-a review. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 13, 128 (2013)Google Scholar
  18. 18.
    Ho, C.-Y., Lin, Y.-D., Lai, Y.-C., Chen, I.-W., Wang, F.-Y., Tai, W.-H.: False positives and negatives from real traffic with intrusion detection/prevention systems. Int. J. Future Comput. Commun. 1, 87 (2012)CrossRefGoogle Scholar
  19. 19.
    Tuck, N., Sherwood, T., Calder, B., Varghese, G.: Deterministic memory-efficient string matching algorithms for intrusion detection. In: Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2004, pp. 2628–2639 (2004)Google Scholar
  20. 20.
    Lu, H., Zheng, K., Liu, B., Zhang, X., Liu, Y.: A memory-efficient parallel string matching architecture for high-speed intrusion detection. IEEE J. Sel. Areas Commun. 24, 1793–1804 (2006)CrossRefGoogle Scholar
  21. 21.
    Aydın, M.A., Zaim, A.H., Ceylan, K.G.: A hybrid intrusion detection system design for computer network security. Comput. Electr. Eng. 35, 517–526 (2009)CrossRefGoogle Scholar
  22. 22.
    ComLaw-Australian Government. Road Transport Legislation – Driving Hours Regulations (2006). https://www.comlaw.gov.au/Details/F2006L00250

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Son Anh Vo
    • 1
  • Joel Scanlan
    • 1
  • Luke Mirowski
    • 1
  • Paul Turner
    • 1
  1. 1.eLogistics Group, EICT SchoolUniversity of TasmaniaHobartAustralia

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