Otwarty dostęp

Classification of traffic signal system anomalies for environment tests of autonomous vehicles


Zacytuj

Barria, J.A., Thajchayapong, S. 2011. Detection and classification of traffic anomalies using microscopic traffic variables, IEEE transactions on intelligent transportation systems, 12(3).10.1109/TITS.2011.2157689Search in Google Scholar

Bruno, L., Parla, G., Celauro, C. 2012. Improved traffic signal detection and classification via image processing algorithms, Procedia - social and behavioral sciences, volume 53.Search in Google Scholar

Csiszár, Cs., Zarkeshev, A. 2017. Demand-capacity coordination method in autonomous public transportation, Transportation research procedia, 27, 784-790.Search in Google Scholar

dpa/muenchen.de, 2016. Signs with geometric figures on black circle (In German Schilder mit geometrischen figuren auf schwarzem kreis).Search in Google Scholar

Evtimov, I., Eykholt, K., Fernandes, E., Kohno, T., Li, B., Prakash, A., Rahmati, A., Song, D. 2017. Robust physical-world attacks on deep learning models, computer vision and pattern recognition (CVPR 2018), Supersedes arxiv preprint, 1707.08945.Search in Google Scholar

Fazekas, Z., Gáspár, P. 2015. Computerized recognition of traffic signs setting out lane arrangements, Acta Polytechnica Hungarica.Search in Google Scholar

Gonzalez, H., Riveiro, B., Armesto, J., Arias, P. 2011. Evaluation of road signs using radiometric information from laser scanning data, Research gate.Search in Google Scholar

Hechri, A. 2011. Lanes and road signs recognition for driver assistance system, IJCSI international journal of computer science issues, vol. 8, issue 6, no 1.Search in Google Scholar

Landaa, J., Prochazkaa, D. 2014. Automatic road inventory using LIDAR, enterprise and the competitive environment, Conference.10.1016/S2212-5671(14)00356-6Search in Google Scholar

lasota, M., skoczylas, M. 2016. Recognitionof multiple traffic signs using keypoints feature detectors, 2016 International conference and exposition on electrical and power engineering (EPE).10.1109/ICEPE.2016.7781397Search in Google Scholar

Munawar, A., Creusot, C. 2015. Structural inpainting of road patches for anomaly detection, MVA2015 IAPR international conference on machine vision applications.10.1109/MVA.2015.7153128Search in Google Scholar

Nyerges, Á., Szalay, Zs. 2017. A new approach for the testing and validation of connected and automated vehicles, 34th International Colloquium on Advanced Manufacturing and Repairing Technologies in Vehicle Industry.Search in Google Scholar

Pintér, K., Szalay, Zs., Gábor, Vida 2017. Autonomous vehicles - novel types and causes of traffic accident, responsibility, 34th international colloquium on advanced manufacturing and repairing technologies in vehicle industry.Search in Google Scholar

Potó, V., Somogyi, Á., Lovas, T., Barsi, Á., Tihanyi, V., Szalay, Zs. 2017. Creating hd map for autonomous vehicles - a pilot study, 34th international colloquium on advanced manufacturing and repairing technologies in vehicle industry.Search in Google Scholar

Road Technical Specifications e-ut-04-02-11-2012-road signs (t) design, application and placement of signboards.Search in Google Scholar

Road Technical Specifications e-ut2-1.113-2001-design of road markings.Search in Google Scholar

Simonite, T. 2018. Even artificial neural networks can have exploitable backdoors, last access time: 2018.04.03.Search in Google Scholar

Stallkamp, J., Schlipsing, M., Slamen, J., Igel, C. 2016. Man vs. computer: benchmarking machine learning algorithms for traffic sign recognition, Neural networks, volume 32, 323-332.Search in Google Scholar

Szalay, Zs., Tettamanti, T., Esztergár-Kiss, D., Varga, Is., Bartolini, C. 2017. Development of a test track for driverless cars vehicle design, track configuration, and liability considerations, Periodica Polytechnica Transportation Engineering. vol 46. no 1.10.3311/PPtr.10753Search in Google Scholar

Szalay, Zs., Tihanyi, V. 2017. Research and development areas related to zalaegerszeg test track, IFFK 2017: xi. innovation and sustainable surface transport, (In Hungarian zalaegerszegi tesztpályához kapcsolódó kutatásfejlesztési területek, IFFK 2017: xi. innováció és fenntartható felszíni közlekedés).Search in Google Scholar

Url: Https://Www.Wired.Com/Story/Machine-Learning-BackdoorsSearch in Google Scholar