Abstract
The paper proposes an approach to driver support in vehicle cabin oriented to dangerous states determination and recommendation generation. To determine dangerous states, we propose to analyze images from smartphone front-facing camera as well as analyze information from accessible sensors. We identified two main dangerous states that are most important to identify to prevent the possible accidents in the roads: drowsiness and distraction. In scope of the approach determined dangerous states are used to generate recommendations for the driver to notify him/her about drowsiness and distraction. Since the attention of the driver should be in the road during the driver, we propose the human-computer interaction interface that is based on speech recognition to interact the driver. Using the interface, the driver interacts with the driver support system to increase the quality of the dangerous state determination and recommendation generation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Markovnikov, N., Kipyatkova, I.: An analytic survey of end-to-end speech recognition systems. SPIIRAS Proc. 3(58), 77–110 (2018)
Schmidt, J., Laarousi, R., Stolzmann, W., Karrer, K.: Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera. Behav. Res. Meth. 50(3), 1088–1101 (2018)
Galarza, E.E., Egas, F.D., Silva, F.M., Velasco, P.M., Galarza, E.D.: Real time driver drowsiness detection based on driver’s face image behavior using a system of human computer interaction implemented in a smartphone. In: Rocha, Á., Guarda, T. (eds.) ICITS 2018. AISC, vol. 721, pp. 563–572. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73450-7_53
Mohammad, F., Mahadas, K., Hung, G.K.: Drowsy driver mobile application: development of a novel scleral-area detection method. Comput. Biol. Med. 89, 76–83 (2017)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision in C ++ with the OpenCV Library, 2nd edn. O’Reilly Media Inc, Sebastopol (2013)
Dasgupta, A., Rahman, D., Routray, A.: A smartphone-based drowsiness detection and warning system for automotive drivers. In: IEEE Transactions on Intelligent Transportation Systems, pp. 1–10 (2018)
García-García, M., Caplier, A., Rombaut, M.: Sleep deprivation detection for real-time driver monitoring using deep learning. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 435–442. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_49
Ramachandran, M., Chandrakala, S.: Android OpenCV based effective driver fatigue and distraction monitoring system. In: 2015 International Conference on Computing and Communications Technologies (ICCCT), pp. 262–266 (2015)
Abulkhair, M., et al.: Mobile platform detect and alerts system for driver fatigue. Procedia Comput. Sci. 62, 555–564 (2015)
García-García, M., Caplier, A., Rombaut, M.: Driver head movements while using a smartphone in a naturalistic context. In: 6th International Symposium on Naturalistic Driving Research, pp. 1–5 (2017)
Qiao, Y., Zeng, K., Xu, L., Yin, X.: A smartphone-based driver fatigue detection using fusion of multiple real-time facial features. In: 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 230–235 (2016)
Kong, W., et al.: A system of driving fatigue detection based on machine vision and its application on smart device. J. Sens. 2015, 1–11 (2015)
Nambi, A.U., et al.: HAMS: Driver and driving monitoring using a Smartphone. In: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking. MobiCom 2018, pp. 840–842. ACM (2018)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)
Smirnov, A., Kashevnik, A., Lashkov, I., Parfenov, V.: Smartphone-based identification of dangerous driving situations: algorithms and implementation. In: Proceedings of the 18th Conference of Open Innovations Association FRUCT, St.Petersburg, pp. 306–313 (2016)
Smirnov, A., Kashevnik, A., Lashkov, I.: Human-smartphone interaction for dangerous situation detection and recommendation generation while driving. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS (LNAI), vol. 9811, pp. 346–353. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_41
Kopinski, T., Geisler, S., Handmann, U.: Contactless interaction for automotive applications. In: Mensch & Computer Workshopband, pp. 87–94 (2013)
Huang, Z., Huang, X.: A study on the application of voice interaction in automotive human machine interface experience design. In: AIP Conference Proceedings, vol. 1955, no. 1, p. 040074 (2018)
Hua, Z., Ng, W.L.: Speech recognition interface design for in-vehicle system. In: Proceedings of the 2nd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 29–33 (2010)
Nass, C., et al.: Improving automotive safety by pairing driver emotion and car voice emotion. In: CHI 2005 Extended Abstracts on Human Factors In Computing Systems, pp. 1973–1976 (2005)
Peissner, M., Doebler, V., Metze, F.: Can voice interaction help reducing the level of distraction and prevent accidents. In: Meta-Study Driver Distraction Voice Interaction, p. 24 (2011)
Owens, J.M., McLaughlin, S.B., Sudweeks, J.: On-road comparison of driving performance measures when using handheld and voice-control interfaces for mobile phones and portable music players. SAE Int. J. Passeng. Cars-Mech. Syst. 3, 734–743 (2010)
Meng, L.J., Wang, Z.Z.: Design and implementation of wireless voice controlled intelligent obstacle-avoiding toy car system. In: 2011 International Conference on Electronics, Communications and Control (ICECC), pp. 1982–1984 (2011)
Kashevnik, A., Lashkov, I.: Decision support system for drivers & passengers: Smartphone-based reference model and evaluation. In: Proceedings of the 23rd Conference of Open Innovations Association FRUCT, pp. 166–171 (2018)
Acknowledgements
The work has been partially financially supported by grants: #17-29-03284, #16-07-00462, and #16-37-60100 of Russian Foundation for Basic Research; by Government of Russian Federation, Grant #08-08; and by the Russian State Research #0073-2019-0005.
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
Kashevnik, A., Lashkov, I., Ryumin, D., Karpov, A. (2019). Smartphone-Based Driver Support in Vehicle Cabin: Human-Computer Interaction Interface. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2019. Lecture Notes in Computer Science(), vol 11659. Springer, Cham. https://doi.org/10.1007/978-3-030-26118-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-26118-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26117-7
Online ISBN: 978-3-030-26118-4
eBook Packages: Computer ScienceComputer Science (R0)