The vehicle driver safety prediction system

• Autorzy: Haller Piotr , Wróbel Radosław , Sierzputowski Gustaw , Dimitrov Radostin , Mihaylow Veselin

Identyfikatory

DOI

10.19206/CE-142950

• Języki publikacji: EN

Abstrakty

EN

The article presents analysis of road crash accidents. It presents the evolution of safety systems, starting from a description of the currently used vehicle-based systems, with particular emphasis on the prediction of the driver falling asleep. The article also proposes a proprietary system of sleep prediction based on the face detection of drivers. The detection of facial landmarks is presented as a two-step process: an algorithm finds faces in general, and then needs to localize key facial structures within the face region of interest.

Słowa kluczowe

EN

safety; system; vehicles; drowsiness; face detection

PL

bezpieczeństwo; system; pojazdy; senność; rozpoznawanie twarzy

• Wydawca: Polskie Towarzystwo Naukowe Silników Spalinowych
• Czasopismo: Combustion Engines
• Rocznik: 2022
• Tom: R. 61, nr 3
• Strony: 11--17
• Opis fizyczny: Bibliogr. 9 poz., il. (w tym kolor.), fot., 1 wykr.

Twórcy

autor

Haller Piotr

• Faculty of Mechanical Engineering, Wroclaw University of Technology

• https://orcid.org/0000-0002-6020-5695

autor

Wróbel Radosław

• Faculty of Mechanical Engineering, Wroclaw University of Technology

• https://orcid.org/0000-0002-8681-0930

autor

Sierzputowski Gustaw

• Faculty of Mechanical Engineering, Wroclaw University of Technology

autor

Dimitrov Radostin

• Faculty of Mechanical Engineering, Varna University of Technology

• https://orcid.org/0000-0001-5260-4816

autor

Mihaylow Veselin

• Faculty of Mechanical Engineering, Varna University of Technology

• https://orcid.org/0000-0002-7381-4414

Bibliografia

• [1] Global Status Report on Road Safety. World Health Organization, 2018. http://apps.who.int/iris/bitstream/handle/10665/277370/WHO-NMH-NVI-18.20-eng.pdf?ua=1
• [2] Centers for Disease Control and Prevention. Information about drowsy driving risks. https://www.cdc.gov/features/dsdrowsydriving/index.html
• [3] SAŁAPATEK, D., DYBAŁA, J., CZAPSKI, P. et al. Driver drowsiness detection systems. Proceedings of the Institute of Vehicles. Warsaw University of Technology. 2017, 3(112), 41-48.
• [4] PyImageSearch. Information about face elements detection. https://www.pyimagesearch.com/2017/04/10/detect-eyes-nose-lips-jaw-dlib-opencv-python
• [5] LEMOV, M.R. Car Safety Wars: One Hundred Years of Technology, Politics, and Death. Fairleigh Dickinson University Press. 2015.
• [6] ROSS, H.L. Functional safety for road vehicles. Springer 2015. https://doi.org/10.1007/978-3-319-33361-8
• [7] OKA, D.K. Building Secure Cars: Assuring the Automotive Software Development Lifecycle. Wiley. 2021. https://doi.org/10.1002/9781119710783.ch3
• [8] LOTHAR, W., SEIFFERT, U. Automotive Safety Handbook. SAE International. 2007. https://doi.org/10.4271/R-377
• [9] AGARWAL, A., YADAV, V. Advanced integrated future vehicle telematics system concept modelling. Global Journal of Technology & Optimization. 2017, 8(2), 1-6. https://doi.org/10.4172/2229-8711.1000215

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).