Abstract
Despite fast progress in the automotive industry, the number of deaths in car accidents is constantly growing. One of the most important challenges in this area, besides crash prevention, is immediate and precise notification of rescue services. Automatic crash detection systems go a long way towards improving these notifications, and new cars currently sold in developed countries often come with such systems factory installed. However, the majority of life threatening accidents occur in low-income countries, where these novel and expensive solutions will not become common anytime soon. This paper presents a method for detecting car collisions, which requires a mobile phone only, and therefore can be used in any type of car. The method was developed and evaluated using data from real crash tests. It integrates data series from various sensors using an optimized decision tree. The evaluation results show that it can successfully detect even minor collisions while keeping the number of false positives at an acceptable level.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Amin, M., Ibne Reaz, M., Bhuiyan, M., Nasir, S.: Kalman filtered gps accelerometer based accident detection and location system: a low-cost approach. Curr. Sci. 106(11), 1548–1554 (2014)
Datentechnik, D.S.: Picdaq5 (2019). http://www.dsd.at/index.php?option=com_content&view=article&id=327:pic-daq-deutsch&catid=53&Itemid=175&lang=de
Commercial Partnership for Development N of Navigation Technologies, U.: Era-glonass (2015). http://en.glonassunion.ru/era-glonass/
Devices, B.C., GmbH, S.: Telematics ecall plug (2019). https://www.bosch-connectivity.com/products/telematics-ecall-plug/
Group, P.: Pzu go (2019). https://www.pzu.pl/pzugo
Jiansheng, F.: Vision-based real-time traffic accident detection. In: Proceeding of the 11th World Congress on Intelligent Control and Automation, pp. 1035–1038 (2014). https://doi.org/10.1109/WCICA.2014.7052859
Octo: Insurance telematics solutions for personal auto (2019). https://www.octousa.com/personal-insurance-telematics-solutions/
OnStar: Welcome to onstar (2019). https://www.onstar.com/us/en/home/
World Health Organization: Global status report on road safety 2018: Summary (2018). https://www.who.int/violence_injury_prevention/road_safety_status/2018/en/
European Parliament: European commission decision 585/2014/eu on the deployment of the interoperable eu-wide ecall services. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32014D0585 (2015)
European Parliament: European commission regulation 2015/758 on ecall type-approval ecall amending directive 2007/46/ec. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32015R0758 (2015)
SpA, S.: Automatic car crash detection app (2015). http://www.sosmartapp.com/
Telematics, C.M.: The drivewell tag (2019). https://www.cmtelematics.com/drivewell-tag/
Thompson, C., White, J., Dougherty, B., Albright, A., Schmidt, D.C.: Using smartphones to detect car accidents and provide situational awareness to emergency responders. In: Mobile Wireless Middleware, Operating Systems, and Applications, pp. 29–42 (2010)
U.S. Department of Transportation: National highway traffic safety administration (2019). https://www.nhtsa.gov/
Visser, M.: Overview on emergency call worldwide - status 2016 (2016). https://www.its-mobility.de/download/eCallDays2016/presentations/Visser_Gemalto_eCallDays_2016.pdf
White, J., Thompson, C., Turner, H., Dougherty, B., Schmidt, D.C.: Wreckwatch: Automatic traffic accident detection and notification with smartphones. Mob. Netw. Appl. 16(3), 285 (2011)
Zaldivar, J., Calafate, C.T., Cano, J.C., Manzoni, P.: Providing accident detection in vehicular networks through obd-ii devices and android-based smartphones. In: 2011 IEEE 36th Conference on Local Computer Networks, pp. 813–819 (2011). https://doi.org/10.1109/LCN.2011.6115556
Öörni, R., Meilikhov, E., Korhonen, T.O.: Interoperability of ecall and era-glonass in-vehicle emergency call systems.IET Intell. Transp. Systems 9(6), 582–590 (2015)
Acknowledgment
This research was partially supported by the Polish National Center for Research and Development under the project no. TANGO2/340869/NCBR/2017 and by the funds of Polish Ministry of Science and Higher Education assigned to AGH University of Science and Technology. We would also like to thank our partners from PZU for supporting the research and the experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Paciorek, M. et al. (2021). Effective Car Collision Detection with Mobile Phone Only. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12747. Springer, Cham. https://doi.org/10.1007/978-3-030-77980-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-77980-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77979-5
Online ISBN: 978-3-030-77980-1
eBook Packages: Computer ScienceComputer Science (R0)