A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction

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

Abstract

Driver distraction is a major challenge in road traffic and major cause of accidents. Vehicle industry dedicates increasing amounts of resources to better quantify the various activities of drivers resulting in distraction. Literature has shown that significant causes for driver distraction are tasks performed by drivers which are not related to driving, like using multimedia interfaces or glancing at co-drivers. One key aspect of the successful implementation of distraction prevention mechanisms is to know when the driver performs such auxiliary tasks. Therefore, capturing these tasks with appropriate measurement equipment is crucial. Especially novel quantification approaches combining data from different sensors and devices are necessary for comprehensively determining causes of driver distraction. However, as a literature review has revealed, there is currently a lack of lightweight frameworks for multi-device integration and multi-sensor fusion to enable cost-effective and minimally obtrusive driver monitoring with respect to scalability and extendibility. This paper presents such a lightweight framework which has been implemented in a demonstrator and applied in a small real-world study involving ten drivers performing simple distraction tasks. Preliminary results of our analysis have indicated a high accuracy of distraction detection for individual distraction tasks and thus the framework’s usefulness. The gained knowledge can be used to develop improved mechanisms for detecting driver distraction through better quantification of distracting tasks.

LanguageEnglish
Title of host publicationAdvanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings
EditorsPaolo Giorgini, Barbara Weber
Place of PublicationBerlin
PublisherSpringer Verlag
Pages80-95
Number of pages16
ISBN (Print)9783030212896
DOIs
StatusPublished - May 2019
Event31st International Conference on Advanced Information Systems Engineering, CAiSE 2019 - Rome, Italy
Duration: 3 Jun 20197 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11483
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on Advanced Information Systems Engineering, CAiSE 2019
CountryItaly
CityRome
Period3/06/197/06/19

Fingerprint

Multisensor Fusion
Sensor data fusion
Driver
Scalability
Accidents
Monitoring
Sensors
Costs
Industry
Quantification
Framework
Literature Review
Multimedia
High Accuracy
Quantify
Traffic

Keywords

  • Driver attention
  • Driver distraction
  • Lightweight framework
  • Multi-device integration
  • Multi-sensor fusion

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lechner, G., Fellmann, M., Festl, A., Kaiser, C., Kalayci, T. E., Spitzer, M., & Stocker, A. (2019). A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. In P. Giorgini, & B. Weber (Eds.), Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings (pp. 80-95). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11483 ). Berlin: Springer Verlag. https://doi.org/10.1007/978-3-030-21290-2_6

A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. / Lechner, Gernot; Fellmann, Michael; Festl, Andreas; Kaiser, Christian; Kalayci, Tahir Emre; Spitzer, Michael; Stocker, Alexander.

Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. ed. / Paolo Giorgini; Barbara Weber. Berlin : Springer Verlag, 2019. p. 80-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11483 ).

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

Lechner, G, Fellmann, M, Festl, A, Kaiser, C, Kalayci, TE, Spitzer, M & Stocker, A 2019, A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. in P Giorgini & B Weber (eds), Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11483 , Springer Verlag, Berlin, pp. 80-95, 31st International Conference on Advanced Information Systems Engineering, CAiSE 2019, Rome, Italy, 3/06/19. https://doi.org/10.1007/978-3-030-21290-2_6
Lechner G, Fellmann M, Festl A, Kaiser C, Kalayci TE, Spitzer M et al. A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. In Giorgini P, Weber B, editors, Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. Berlin: Springer Verlag. 2019. p. 80-95. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-21290-2_6
Lechner, Gernot ; Fellmann, Michael ; Festl, Andreas ; Kaiser, Christian ; Kalayci, Tahir Emre ; Spitzer, Michael ; Stocker, Alexander. / A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. Advanced Information Systems Engineering - 31st International Conference, CAiSE 2019, Proceedings. editor / Paolo Giorgini ; Barbara Weber. Berlin : Springer Verlag, 2019. pp. 80-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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