Video detection data as important factor for transport systems safety improvement

• Autorzy: Mazurkiewicz J.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the analysis and discussion of video detection data usage for discrete transport systems safety improvement. The Autonomous Vehicles (AV) and the Augmented Reality (AR) research in connection with a Driving Assistance (DA) are presented. This article is going to show where the border between those two fields of interest is and how they are going to influence on the future of automotive. The proposal of the AR system – based on soft-computing methods used for an object classification problem – is given. The input data are taken from the real traffic monitoring system located at the set of roads in Poland. Data from the monitoring devices are used to analyze the travel time of vehicles – elements of the transportation system. The travel time model taking into account the real road situation is built. The proposed solution can be an essential tool for the owner and administrator of the transportation systems

Słowa kluczowe

EN

discrete transport system; road monitoring; vehicle recognition; Augmented Reality; AR; autonomous vehicles; AV; ANPR; MMR

• Wydawca: Polskie Towarzystwo Bezpieczeństwa i Niezawodności
• Czasopismo: Journal of Polish Safety and Reliability Association
• Rocznik: 2016
• Tom: Vol. 7, No. 1
• Strony: 167--176
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wykr.

Twórcy

autor

Mazurkiewicz J.

• Wroclaw University of Science and Technology, Faculty of Electronics, Poland

Bibliografia

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• [6] DARPA Urban Challenge. (2013), [available at: http://archive.darpa.mil/grandchallenge/ index.asp].
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• [8] Geronimo, D., Lopez, A. M., Sappa, A. D. et al. (2010). Survey of Pedestrian Detection for Advanced Driver Assistance Systems. IEEE Transactions on Pattern Analysis And Machine Intelligenc, 32, 7.
• [9] Leduc, G. (2008) Road Traffic Data: Collection Methods and Applications. Working Papers JRC 47967 Institute for Prospective and Technological Studies, Joint Research Centre, 1-55.
• [10] Li, H. & Nashashibi, F. (2011) Multi-vehicle Cooperative Perception and Augmented Reality for Driver Assistance: A Possibility to "See" Through Front Vehicle. 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, 78-89.
• [11] Lin, J. H., Lin, C. M., Dow, C. R et al. (2011) Design and Implement Augmented Reality for Supporting Driving Visual Guidance. Second International Conference on Innovations in Bioinspired Computing and Applications. IEEE Computer Association, 105-123.
• [12] Lindner, F., Kressel, U. & Kaelberer, S. (2004) Robust Recognition of Traffic Signals. IEEE Intelligent Vehicles Symposium. University of Parma.
• [13] Narzt, W., Pomberger, G., Ferscha, A. et al. (2005) Augmented reality navigation systems. Springer-Verlag.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).