TY - JOUR AU - Péter, Tamás AU - Bóta, János László AU - Götz, Sándor TI - Increasing Navigation Safety by Introducing the Modern DunaInfoControl Control System JF - PERIODICA POLYTECHNICA TRANSPORTATION ENGINEERING J2 - PERIOD POLYTECH TRANSP ENG VL - 2025 PY - 2025 SP - 1 EP - 14 PG - 14 SN - 0303-7800 DO - 10.3311/PPtr.40262 UR - https://m2.mtmt.hu/api/publication/36206321 ID - 36206321 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Péter, Tamás TI - Nonlinear parametric modelling of road traffic processes on large networks JF - INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS J2 - INT J HEAVY VEH SYST VL - 1 PY - 2025 IS - 1 SP - 1 EP - 12 PG - 12 SN - 1744-232X DO - 10.1504/IJHVS.2025.10071716 UR - https://m2.mtmt.hu/api/publication/36213983 ID - 36213983 LA - English DB - MTMT ER - TY - JOUR AU - Péter, Tamás AU - Lakatos, István TI - Equivalence classification of spatial nonlinear vehicle swing systems considering vertical swings JF - International Journal of Optimization and Control: Theories & Applications (IJOCTA) J2 - Int J Optim Control VL - 2025 PY - 2025 SP - 1 EP - 15 PG - 15 SN - 2146-0957 UR - https://m2.mtmt.hu/api/publication/36290994 ID - 36290994 LA - English DB - MTMT ER - TY - BOOK ED - Péter, Tamás TI - Innováció és fenntartható felszíni közlekedés: IFFK 2025 PB - Magyar Mérnökakadémia (MMA) CY - Budapest PY - 2025 SN - 9786158268806 UR - https://m2.mtmt.hu/api/publication/36860759 ID - 36860759 LA - English DB - MTMT ER - TY - CHAP AU - Kocsis, Bence AU - Lakatos, István AU - Péter, Tamás ED - Péter, Tamás TI - Matematikai módszerek a közúti közlekedési folyamatok kiterjesztett modellezésében (Mathematical Methods in the Extended Modelling of Road Traffic Processes) T2 - Innováció és fenntartható felszíni közlekedés: IFFK 2025 PB - Magyar Mérnökakadémia (MMA) CY - Budapest SN - 9786158268806 PY - 2025 UR - https://m2.mtmt.hu/api/publication/36860766 ID - 36860766 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Kocsis, Bence AU - Lakatos, István AU - Péter, Tamás ED - Péter, Tamás TI - Járműflotta-adatok értékelése és felhasználási lehetőségei a közlekedési folyamatok modellezésében (Evaluation and Application of Vehicle Fleet Data in Traffic Process Modelling) T2 - Innováció és fenntartható felszíni közlekedés: IFFK 2025 PB - Magyar Mérnökakadémia (MMA) CY - Budapest SN - 9786158268806 PY - 2025 UR - https://m2.mtmt.hu/api/publication/36860793 ID - 36860793 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Rózsás , Zoltán AU - Lakatos, István AU - Péter, Tamás TI - Building a Training Dataset for Machine Learning, Radar-Based Pedestrian Detection JF - INTERNATIONAL JOURNAL OF AUTOMOTIVE SCIENCE AND TECHNOLOGY J2 - INT J AUTOMOT SCI TECH VL - 9 PY - 2025 IS - 1st Future of Vehicles Conf. SP - 72 EP - 76 PG - 5 SN - 2587-0963 DO - 10.30939/ijastech..1756258 UR - https://m2.mtmt.hu/api/publication/36940923 ID - 36940923 AB - 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. LA - English DB - MTMT ER - TY - BOOK ED - Péter, Tamás TI - XVIII. Innováció és fenntartható felszíni közlekedés konferencia, XVIII. IFFK 2024 PB - Magyar Mérnökakadémia (MMA) CY - Budapest PY - 2024 SN - 9789638887597 UR - https://m2.mtmt.hu/api/publication/35345679 ID - 35345679 LA - Hungarian DB - MTMT ER - TY - BOOK AU - Carla, Hermida AU - Manuela, Cordero AU - Adriana, Quezada AU - Daniel, Orellana AU - Enrique, Flores-Juca AU - Jessica, Chica AU - Estefanía, Mora-Arias AU - María, Elisa Bustos AU - Mateo, Marín AU - Natasha, Cabrera AU - Augusta, Hermida AU - Patricia, Cazorla AU - Elina, ÁvilaOrdóñez AU - Jairo, Ortega AU - János, Tóth AU - Péter, Tamás AU - Martín, Ortega AU - Lisseth, Molina AU - Paúl, Arévalo AU - Antonio, Cano AU - Vinicio, Iñiguez-Morán AU - Danny, OchoaCorrea AU - Juan, Leonardo Espinoza AU - Francisco, Jurado AU - Néstor, Rivera AU - Juan, Molina AU - Andrea, Bermeo AU - Gina, Novillo AU - Xavier, Serrano-Guerrero AU - Antonio, Barragán-Escandón AU - Esteban, Zalamea-León AU - Gustavo, Álvarez-Coello AU - Andrés, Baquero-Larriva AU - Mateo, Coello-Salcedo AU - Daniel, Cordero-Moreno AU - Efrén, Fernández-Palomeque AU - Robert, Rockwood-Iglesias AU - Francisco, TorresMoscoso AU - Diego, Morales Jadán AU - Marco, Toledo Orozco AU - Javier, Cabrera Mejía TI - LA CIUDAD SE MUEVE ASÍ PB - Centro Editorial UCuenca Press CY - Cuenca PY - 2024 SN - 9789978145364 ; 9789978145463 UR - https://m2.mtmt.hu/api/publication/35576600 ID - 35576600 LA - Spanish DB - MTMT ER - TY - CHAP AU - Jairo, Ortega AU - János, Tóth AU - Péter, Tamás AU - Martín, Ortega TI - El sistema de park and ride en el entorno urbano de una ciudad media T2 - LA CIUDAD SE MUEVE ASÍ PB - Centro Editorial UCuenca Press CY - Cuenca SN - 9789978145364 ; 9789978145463 PY - 2024 SP - 67 EP - 79 PG - 13 UR - https://m2.mtmt.hu/api/publication/35576612 ID - 35576612 LA - Spanish DB - MTMT ER -