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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
NHTSA Risky Driving. https://www.nhtsa.gov/risky-driving/distracted-driving. Accessed 20 Nov 2018
Study on good practices for reducing road safety risks caused by road user distractions. https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/behavior/distraction_study.pdf. Accessed 20 Nov
Research Note on Distracted Driving. https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812517. Accessed 20 Nov
Thomas, P., Morris, A., Talbot, R., Fagerlind, H.: Identifying the causes of road crashes in Europe. Ann. Adv. Automot. Med. 57(13), 13–22 (2013)
Payre, W., Cestac, J., Dang, N.-T., Vienne, F., Delhomme, P.: Impact of training and in-vehicle task performance on manual control recovery in an automated car. Transp. Res. Part F: Traffic Psychol. Behav. 46(A), 216–227 (2017)
Zeeb, K., Buchner, A., Schrauf, M.: What determines the take-over time? An integrated model approach of driver take-over after automated driving. Accid. Anal. Prev. 78, 212–221 (2015)
Youtube Driver Distraction. https://www.youtube.com/watch?v=yVbPGmsG5sI. Accessed 20 Nov 2018
Youtube Understanding Driver Distraction. https://www.youtube.com/watch?v=XToWVxS_9lA. Accessed 20 Nov 2018
Detection of Driver Distraction. http://ppms.cit.cmu.edu/media/project_files/UTC_project_13_Multimodal_Detection_of_Driver_Distraction_-_final_report.pdf. Accessed 20 Nov 2018
Zendrive Distracted Driving. https://d1x6dm64pjo2h2.cloudfront.net/casestudies/Zendrive_Distracted_Driving_2018.pdf. Accessed 20 Nov 2018
Regan, M.A., Hallett, C., Gordon, C.P.: Driver distraction and driver inattention: definition, relationship and taxonomy. Accid. Anal. Prev. 43(5), 1771–1781 (2011)
Sussman, E.D., Bishop, H., Madnick, B., Walters, R.: Driver inattention and highway safety. Transp. Res. Rec. 1047, 40–48 (1985)
Young, K., Regan, M.: Driver distraction: a review of the literature. In: Faulks, I.J., Regan, M., Stevenson, M., Brown, J., Porter, A., Irwin, J.D. (eds.) Distracted driving, pp. 379–405. Australasian College of Road Safety, Sydney (2007)
Dong, Y., Hu, Z., Uchimura, K., Murayama, N.: Driver inattention monitoring system for intelligent vehicles: a review. IEEE Trans. Intell. Transp. Syst. 12(2), 596–614 (2011)
Horberry, T., Anderson, J., Regan, M.A., Triggs, T.J., Brown, J.: Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accid. Anal. Prev. 38(1), 185–191 (2006)
Klauer, S., Dingus, T., Neale, V., Sudweeks, J., Ramsey, D.: The Impact of Driver Inattention on Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data. Virginia Tech Transportation Institute, Blacksburg (2006)
D’Orazio, T., Leo, M., Guaragnella, C., Distante, A.: A visual approach for driver inattention detection. Pattern Recogn. 40(8), 2341–2355 (2007)
Fletcher, L., Zelinsky, A.: Driver inattention detection based on eye gaze—road event correlation. Int. J. Robot. Res. 28(6), 774–801 (2009)
Swan, M.: Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 6(2), 492–525 (2009)
Swan, M.: The quantified self: fundamental disruption in big data science and biological discovery. Big data 1(2), 85–99 (2013)
Stocker, A., Kaiser, C., Fellmann, M.: Quantified vehicles. Bus. Inf. Syst. Eng. 59(2), 125–130 (2017)
CAN DBC File – Convert Data in Real Time. https://www.csselectronics.com/screen/page/dbc-database-can-bus-conversion-wireshark-j1939-example/language/en. Accessed 27 Nov 2018
SAE J1939 Standards Collection on the Web: Content. https://www.sae.org/standardsdev/groundvehicle/j1939a.htm. Accessed 27 Nov 2018
Pandian, P.S., Mohanavelu, K., Safeer, K.P., Kotresh, T.M., Shakunthala, D.T., Gopal, P., Padaki, V.C.: Smart Vest: wearable multi-parameter remote physiological monitoring system. Med. Eng. Phys. 30(4), 466–477 (2008)
Gellersen, H.W., Schmidt, A., Beigl, M.: Multi-sensor context-awareness in mobile devices and smart artifacts. Mob. Netw. Appl. 7(5), 341–351 (2002)
Ramos, F.B.A., Lorayne, A., Costa, A.A.M., de Sousa, R.R., et al.: Combining smartphone and smartwatch sensor data in activity recognition approaches: an experimental evaluation. In: Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016, Redwood City, pp. 267–272 (2016)
Shoaib, M., Bosch, S., Scholten, H., Havinga, P.J., Incel, O.D.: Towards detection of bad habits by fusing smartphone and smartwatch sensors. In: Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), St. Louis, pp. 591–596 (2015)
Vilarinho, T., Farshchian, B., Bajer, D.G., Dahl, O.H., et al.: A combined smartphone and smartwatch fall detection system. In: Proceedings of the 2015 IEEE International Conference on Computer and Information Technology, Liverpool, pp. 1443–1448 (2015)
Casilari, E., Santoyo-Ramón, J.A., Cano-García, J.M.: Analysis of a smartphone-based architecture with multiple mobility sensors for fall detection. PLoS ONE 11(12), e0168069 (2016)
Giang, W.C., Shanti, I., Chen, H.Y.W., Zhou, A., Donmez, B.: Smartwatches vs. smartphones: a preliminary report of driver behavior and perceived risk while responding to notifications. In: Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Nottingham, pp. 154–161 (2015)
Liu, L., Karatas, C., Li, H., Tan, S., et al.: Toward detection of unsafe driving with wearables. In: Proceedings of the 2015 workshop on Wearable Systems and Applications, Florence, pp. 27–32 (2015)
De Arriba-Pérez, F., Caeiro-Rodríguez, M., Santos-Gago, J.M.: Collection and processing of data from wrist wearable devices in heterogeneous and multiple-user scenarios. Sensors 16(9), 1538 (2016)
Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., et al.: Jupyter notebooks – a publishing format for reproducible computational workflows. In: Proceedings of the 20th International Conference on Electronic Publishing, pp. 87–90. IOS Press, Goettingen (2016)
McKinney, W.: Data structures for statistical computing in Python. In: Proceedings of the 9th Python in Science Conference, Austin, pp. 51–56 (2010)
Oliphant, T.E.: Guide to NumPy, 2nd edn. CreateSpace Independent Publishing Platform, USA (2015)
Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time series FeatuRe extraction on basis of scalable hypothesis tests. Neurocomputing 307, 72–77 (2018)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Kaiser, C., Stocker, A., Festl, A., Lechner, G., Fellmann, M.: A research agenda for vehicle information systems. ECIS (2018)
Acknowledgements
Parts of this study were funded by the Austrian Research Promotion Agency (FFG) under project number 866781 (FFG FEMTech Project GENDrive). The authors would further like to acknowledge the financial support of the COMET K2 – Competence Centers for Excellent Technologies Programme of the Federal Ministry for Transport, Innovation and Technology (bmvit), the Federal Ministry for Digital, Business and Enterprise (bmdw), the Austrian Research Promotion Agency (FFG), the Province of Styria and the Styrian Business Promotion Agency (SFG).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lechner, G. et al. (2019). A Lightweight Framework for Multi-device Integration and Multi-sensor Fusion to Explore Driver Distraction. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-21290-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21289-6
Online ISBN: 978-3-030-21290-2
eBook Packages: Computer ScienceComputer Science (R0)