@article{MTMT:36206321,
	title = {Increasing Navigation Safety by Introducing the Modern DunaInfoControl Control System},
	url = {https://m2.mtmt.hu/api/publication/36206321},
	author = {Péter, Tamás and Bóta, János László and Götz, Sándor},
	doi = {10.3311/PPtr.40262},
	journal-iso = {PERIOD POLYTECH TRANSP ENG},
	journal = {PERIODICA POLYTECHNICA TRANSPORTATION ENGINEERING},
	volume = {2025},
	unique-id = {36206321},
	issn = {0303-7800},
	abstract = {The study presents a development proposal coordinated by the Hungarian Academy of Engineering (MMA) for intelligent navigation management on the Danube. We analyse the introduction of a security-enhancing central control system based on the IDM model. The publication reviews the main issues related to navigation safety in Hungary. Without claiming to be exhaustive, in the field of water transport control theory and practical applications, important players include HUN-REN SZTAKI, Budapest University of Technology and Economics, Széchenyi István University, HungaroControl Zrt. and the Water Police. This development requires combining modern control theory and a high-capacity 5G network. The program proposes the implementation of intelligent control in the first phase in the Budapest area, between river km 1660 in the north and river km 1633 in the south. This is because accidents much larger than the "Hableány" boat tragedy that occurred in Budapest on the evening of May 29, 2019, at 9:05 p.m., may occur in this section in the future. Consider, for example, the appearance of convoys transporting 6000-8000 tons of dangerous goods on the Danube. These represent a very serious source of danger in the event of collisions with the Budapest bridge piers. The proposal also intends to support the RSOE river IT system (developed by NOVOFER Zrt.), which has been built and operating since the end of 2018.},
	year = {2025},
	eissn = {1587-3811},
	pages = {1-14}
}

@article{MTMT:36213983,
	title = {Nonlinear parametric modelling of road traffic processes on large networks},
	url = {https://m2.mtmt.hu/api/publication/36213983},
	author = {Péter, Tamás},
	doi = {10.1504/IJHVS.2025.10071716},
	journal-iso = {INT J HEAVY VEH SYST},
	journal = {INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS},
	volume = {1},
	unique-id = {36213983},
	issn = {1744-232X},
	year = {2025},
	eissn = {1741-5152},
	pages = {1-12}
}

@article{MTMT:36290994,
	title = {Equivalence classification of spatial nonlinear vehicle swing systems considering vertical swings},
	url = {https://m2.mtmt.hu/api/publication/36290994},
	author = {Péter, Tamás and Lakatos, István},
	journal-iso = {Int J Optim Control},
	journal = {International Journal of Optimization and Control: Theories & Applications (IJOCTA)},
	volume = {2025},
	unique-id = {36290994},
	issn = {2146-0957},
	year = {2025},
	eissn = {2146-5703},
	pages = {1-15},
	orcid-numbers = {Lakatos, István/0000-0002-3855-7379}
}

@book{MTMT:36860759,
	title = {Innováció és fenntartható felszíni közlekedés: IFFK 2025},
	url = {https://m2.mtmt.hu/api/publication/36860759},
	isbn = {9786158268806},
	editor = {Péter, Tamás},
	publisher = {MMA},
	unique-id = {36860759},
	year = {2025}
}

@inproceedings{MTMT:36860766,
	title = {Matematikai módszerek a közúti közlekedési folyamatok kiterjesztett modellezésében (Mathematical Methods in the Extended Modelling of Road Traffic Processes)},
	url = {https://m2.mtmt.hu/api/publication/36860766},
	author = {Kocsis, Bence and Lakatos, István and Péter, Tamás},
	booktitle = {Innováció és fenntartható felszíni közlekedés: IFFK 2025},
	unique-id = {36860766},
	year = {2025},
	orcid-numbers = {Lakatos, István/0000-0002-3855-7379}
}

@inproceedings{MTMT:36860793,
	title = {Járműflotta-adatok értékelése és felhasználási lehetőségei a közlekedési folyamatok modellezésében (Evaluation and Application of Vehicle Fleet Data in Traffic Process Modelling)},
	url = {https://m2.mtmt.hu/api/publication/36860793},
	author = {Kocsis, Bence and Lakatos, István and Péter, Tamás},
	booktitle = {Innováció és fenntartható felszíni közlekedés: IFFK 2025},
	unique-id = {36860793},
	year = {2025},
	orcid-numbers = {Lakatos, István/0000-0002-3855-7379}
}

@article{MTMT:36940923,
	title = {Building a Training Dataset for Machine Learning, Radar-Based Pedestrian Detection},
	url = {https://m2.mtmt.hu/api/publication/36940923},
	author = {Rózsás , Zoltán and Lakatos, István and Péter, Tamás},
	doi = {10.30939/ijastech..1756258},
	journal-iso = {INT J AUTOMOT SCI TECH},
	journal = {INTERNATIONAL JOURNAL OF AUTOMOTIVE SCIENCE AND TECHNOLOGY},
	volume = {9},
	unique-id = {36940923},
	issn = {2587-0963},
	abstract = {Abstract: Radar-based pedestrian detection is a key enabler of advanced driver-assistance systems (ADAS) and future autonomous driving functions, particularly under conditions where vision sensors are limited by poor lighting or adverse weather. In this study, we present a structured dataset collected at the ZalaZONE proving ground, a state-of-the-art automotive testing facility in Hungary, using a Continental ARS 408‑21 automotive radar operating at 77 GHz . The dataset contains static radar measurements of both a real human subject and a pedestrian dummy, recorded at multiple controlled distances (5 m and 10 m) and orientations (front-facing and side-facing). Each radar scan includes radar cross-section (RCS), object distance, relative velocity, and metadata retrieved directly from the vehicle’s CAN bus interface. The results demonstrate that RCS values strongly depend on target type, orientation, and distance. Real pedestrians produce significantly higher variance due to posture, clothing materials, and micro‑movements, whereas dummy mannequins exhibit stable, narrow RCS distributions. Interestingly, measurements at longer distances show less negative RCS values, likely influenced by multipath reflections and environmental dispersion effects. The dataset is fully labeled and formatted for direct use in supervised machine learning pipelines, supporting classification models such as logistic regression, support vector machines, and neural networks. It also pro-vides a foundation for future extensions with dynamic scenes and temporal sequence modeling, enabling the development of more robust and generalizable radar-based pedestrian detection algorithms. By combining controlled measurements with realistic environmental variability, this dataset contributes to the advancement of radar sensing technologies for safe and reliable autonomous driving. Quantitatively, the mean RCS difference between real pedestrians and dummy targets reached 3.8 dB at 5 m and 4.5 dB at 10 m, confirming the discriminative potential of the dataset for classification tasks.},
	year = {2025},
	pages = {72-76},
	orcid-numbers = {Lakatos, István/0000-0002-3855-7379}
}

@book{MTMT:35345679,
	title = {XVIII. Innováció és fenntartható felszíni közlekedés konferencia, XVIII. IFFK 2024},
	url = {https://m2.mtmt.hu/api/publication/35345679},
	isbn = {9789638887597},
	editor = {Péter, Tamás},
	publisher = {MMA},
	unique-id = {35345679},
	year = {2024}
}

@book{MTMT:35576600,
	title = {LA CIUDAD SE MUEVE ASÍ},
	url = {https://m2.mtmt.hu/api/publication/35576600},
	isbn = {9789978145364 ; 9789978145463},
	author = {Carla, Hermida and Manuela, Cordero and Adriana, Quezada and Daniel, Orellana and Enrique, Flores-Juca and Jessica, Chica and Estefanía, Mora-Arias and María, Elisa Bustos and Mateo, Marín and Natasha, Cabrera and Augusta, Hermida and Patricia, Cazorla and Elina, ÁvilaOrdóñez and Jairo, Ortega and János, Tóth and Péter, Tamás and Martín, Ortega and Lisseth, Molina and Paúl, Arévalo and Antonio, Cano and Vinicio, Iñiguez-Morán and Danny, OchoaCorrea and Juan, Leonardo Espinoza and Francisco, Jurado and Néstor, Rivera and Juan, Molina and Andrea, Bermeo and Gina, Novillo and Xavier, Serrano-Guerrero and Antonio, Barragán-Escandón and Esteban, Zalamea-León and Gustavo, Álvarez-Coello and Andrés, Baquero-Larriva and Mateo, Coello-Salcedo and Daniel, Cordero-Moreno and Efrén, Fernández-Palomeque and Robert, Rockwood-Iglesias and Francisco, TorresMoscoso and Diego, Morales Jadán and Marco, Toledo Orozco and Javier, Cabrera Mejía},
	publisher = {Centro Editorial UCuenca Press},
	unique-id = {35576600},
	year = {2024}
}

@{MTMT:35576612,
	title = {El sistema de park and ride en el entorno urbano de una ciudad media},
	url = {https://m2.mtmt.hu/api/publication/35576612},
	author = {Jairo, Ortega and János, Tóth and Péter, Tamás and Martín, Ortega},
	booktitle = {LA CIUDAD SE MUEVE ASÍ},
	unique-id = {35576612},
	year = {2024},
	pages = {67-79}
}