TY - JOUR AU - Szántó, Mátyás AU - Vajta, László TI - Forecasting critical weather front transitions based on locally measured meteorological data JF - IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE J2 - IDŐJÁRÁS VL - 127 PY - 2023 IS - 4 SP - 459 EP - 471 PG - 13 SN - 0324-6329 DO - 10.28974/idojaras.2023.4.3 UR - https://m2.mtmt.hu/api/publication/34417880 ID - 34417880 N1 - Export Date: 21 December 2023 Correspondence Address: Szántó, M.; Department of Control Engineering and Information Technology, Műegyetem rkp. 3, Hungary; email: mszanto@iit.bme.hu Funding Agency and Grant Number: European Union [RRF-2.3.1-21-2022-00004] Funding text: The authors would like to thank Dr. Kalman Kovacs and the Data Supply Department of the Hungarian Meteorological Service for sharing the extensive local and medical meteorological and accident databases for the purpose of the research presented in this paper. The research presented here was supported by the the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. AB - Certain types of medical meteorological phenomenontransitions can have a significant deteriorating effect on road safety conditions. Hence, a system that is capable of warning road users of the possibility of such conversions can prove to be utterly useful. Vehicles on different levels of automation (i.e., ones equipped with driver assistance systems – DAS) can use this information to adjust their parameters and become more cautious or warn the drivers to be more careful while driving. In this paper, we prove that identifying the critical type of weather front transition (i.e., no front to unstable cold front) is possible based on locally observable meteorological information. We present our method for classifying weather front transitions to non-critical versus critical types. Our developed machine learning model was trained on a dataset covering 10 years of meteorological data in Hungary, and it shows promising results with a recall value of 86%, and an F1-score of 60%. As the developed method will form the basis of a patent, we are omitting key components and parameters of our solution from this paper. LA - English DB - MTMT ER - TY - CHAP AU - Faghihi, T. AU - Sabzi, Shahab AU - Vajta, László TI - Energy Management Strategy based Charging Coordination for Electric Vehicle Integrated Distribution Grid T2 - 2023 International Conference on Future Energy Solutions (FES) PB - IEEE CY - Piscataway (NJ) SN - 9798350332308 PY - 2023 SP - 1 EP - 6 PG - 6 DO - 10.1109/FES57669.2023.10182883 UR - https://m2.mtmt.hu/api/publication/34094269 ID - 34094269 N1 - Export Date: 14 August 2023 Correspondence Address: Faghihi, T.; Budapest University of Technology and Economics, Hungary; email: tayebeh.faghihi@edu.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Hidalgo, C. AU - Gonzalez, L. AU - Perez, J. AU - Asua, E. AU - Vajta, László TI - Trajectory Planning of Automated Vehicles Using Real-Time Map Updates JF - IEEE ACCESS J2 - IEEE ACCESS VL - 11 PY - 2023 SP - 67468 EP - 67481 PG - 14 SN - 2169-3536 DO - 10.1109/ACCESS.2023.3291350 UR - https://m2.mtmt.hu/api/publication/34069746 ID - 34069746 N1 - Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Budapest, Hungary Industry and Mobility Area, Basque Research and Technology Alliance, Tecnalia, Arteaga street, 25, Derio, Bizkaia, Spain Department of Electricity and Electronics, University of the Basque Country, Sarriena, s/n, Leioa, Vizcaya, Spain LA - English DB - MTMT ER - TY - CHAP AU - Szántó, Mátyás AU - Vajta, László ED - Nathanail, Eftihia G. ED - Gavanas, Nikolaos ED - Adamos, Giannis TI - Relationship and Differences Between Entrepreneurship and Research in the CrowdMapping Project for Crowdsourced Urban Data T2 - Smart Energy for Smart Transport PB - Springer Nature Switzerland AG CY - Cham SN - 9783031237218 T3 - Lecture Notes in Intelligent Transportation and Infrastructure, ISSN 2523-3440 PY - 2023 SP - 531 EP - 541 PG - 11 DO - 10.1007/978-3-031-23721-8_44 UR - https://m2.mtmt.hu/api/publication/33695726 ID - 33695726 N1 - Export Date: 2 October 2023 Correspondence Address: Szántó, M.; Department of Control Engineering and Information Technology, Hungary; email: mszanto@iit.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Kobál, Sándor AU - Vajta, László AU - Horváth, Viktor Győző AU - Lógó, János Máté AU - Barsi, Árpád TI - Building Maps Using Monocular Image-feeds from Windshield-mounted Cameras in a Simulator Environment JF - PERIODICA POLYTECHNICA-CIVIL ENGINEERING J2 - PERIOD POLYTECH CIV ENG VL - 67 PY - 2023 IS - 2 SP - 457 EP - 472 PG - 16 SN - 0553-6626 DO - 10.3311/PPci.21500 UR - https://m2.mtmt.hu/api/publication/33609760 ID - 33609760 N1 - Funding Agency and Grant Number: European Union [RRF-2.3.1-21-2022-00004]; Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund [TKP2021, BME-NVA-02] Funding text: The research was supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Artificial Intelligence National Laboratory. The research reported in this paper is part of project no. BME-NVA-02, implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021 funding scheme. AB - 3-dimensional, accurate, and up-to-date maps are essential for vehicles with autonomous capabilities, whose functionality is made possible by machine learning-based algorithms. Since these solutions require a tremendous amount of data for parameter optimization, simulation-to-reality (Sim2Real) methods have been proven immensely useful for training data generation. For creating realistic models to be used for synthetic data generation, crowdsourcing techniques present a resource-efficient alternative. In this paper, we show that using the Carla simulation environment, a crowdsourcing model can be created that mimics a multi-agent data gathering and processing pipeline. We developed a solution that yields dense point clouds based on monocular images and location information gathered by individual data acquisition vehicles. Our method provides scene reconstructions using the robust Structure-from-Motion (SfM) solution of Colmap. Moreover, we introduce a solution for synthesizing dense ground truth point clouds originating from the Carla simulator using a simulated data acquisition pipeline. We compare the results of the Colmap reconstruction with the reference point cloud after aligning them using the iterative closest point algorithm. Our results show that a precise point cloud reconstruction was feasible with this crowdsourcing-based approach, with 54\% of the reconstructed points having an error under 0.05 m, and a weighted root mean square error of 0.0449 m for the entire point cloud. LA - English DB - MTMT ER - TY - JOUR AU - Sabzi, Shahab AU - Vajta, László TI - Security and Energy Consumption Considerations of Electric Vehicles Integration in Smart Grids JF - U.Porto Journal of Engineering J2 - UPjeng VL - 9 PY - 2023 IS - 1 SP - 134 EP - 149 PG - 16 SN - 2183-6493 DO - 10.24840/2183-6493_009-001_001382 UR - https://m2.mtmt.hu/api/publication/32839439 ID - 32839439 LA - English DB - MTMT ER - TY - CHAP AU - Tayebeh, Faghihi AU - Sabzi, Shahab AU - Vajta, László ED - Kiss, Bálint ED - Szirmay-Kalos, László TI - Effects of electric vehicles and PV units on the distribution network, a modified IEEE 31 buses distribution network case study T2 - Proceedings of the Workshop on the Advances in Information Technology 2022 PB - OSZK CY - Budapest SN - 9789634218715 PY - 2022 SP - 151 EP - 156 PG - 6 UR - https://m2.mtmt.hu/api/publication/33538477 ID - 33538477 LA - English DB - MTMT ER - TY - JOUR AU - Szántó, Mátyás AU - Bogár, György Richárd AU - Vajta, László TI - ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping JF - PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE J2 - PERIOD POLYTECH ELECTR ENG COMP SCI VL - 66 PY - 2022 IS - 3 SP - 236 EP - 247 PG - 12 SN - 2064-5260 DO - 10.3311/PPee.20437 UR - https://m2.mtmt.hu/api/publication/32937203 ID - 32937203 N1 - Export Date: 18 August 2022 Correspondence Address: Szántó, M.; Department of Control Engineering and Information Technology, 2 Magyar tudosok Blvd., Hungary; email: mszanto@iit.bme.hu AB - In this paper, a novel solution is introduced for visual Simultaneous Localization and Mapping (vSLAM) that is built up of Deep Learning components. The proposed architecture is a highly modular framework in which each component offers state of the art results in their respective fields of vision-based Deep Learning solutions. The paper shows that with the synergic integration of these individual building blocks, a functioning and efficient all-through deep neural (ATDN) vSLAM system can be created. The Embedding Distance Loss function is introduced and using it the ATDN architecture is trained. The resulting system managed to achieve 4.4% translation and 0.0176 deg/m rotational error on a subset of the KITTI dataset. The proposed architecture can be used for efficient and low-latency autonomous driving (AD) aiding database creation as well as a basis for autonomous vehicle (AV) control. LA - English DB - MTMT ER - TY - JOUR AU - Sabzi, Shahab AU - Vajta, László AU - Faghihi, Tayebeh TI - A Review on Electric Vehicles Charging Strategies Concerning Actors Interests JF - PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE J2 - PERIOD POLYTECH ELECTR ENG COMP SCI VL - 66 PY - 2022 IS - 2 SP - 148 EP - 162 PG - 15 SN - 2064-5260 DO - 10.3311/PPee.19625 UR - https://m2.mtmt.hu/api/publication/32818781 ID - 32818781 N1 - Export Date: 15 July 2022 Correspondence Address: Sabzi, S.; Department of Control Engineering and Information Technology, Műegyetem rkp. 3., Hungary; email: sabzi@iit.bme.hu AB - Electric vehicles are becoming increasingly popular in societies and an important part of smart grids. Utility companies should be able to provide them with vital energy as they need electric energy instead of fuel, and this is where new challenges emerge in the network. In order to avoid causing utilities to incur additional energy and economic losses, researchers have proposed smart charging as a way to provide adequate energy to vehicles. When developing a charging schedule for a fleet of EVs, special considerations are made on variables such as energy, cost, and EVs milage. In this review paper, the importance of EVs integration into smart grids is studied, and then different methods to develop EVs charging scheduling are investigated. These methods can vary from optimization algorithms to learning-based, and game theory-based approaches. Then, as the considered system consists of three main actors, including EV users, the utility operator, and aggregators, a systematic review is conducted on these actors, and objectives related to each one are analyzed. Finally, research gaps related to the problem are studied. Researchers can use this review to conduct further research on the integration of EVs into smart grids. LA - English DB - MTMT ER - TY - CHAP AU - Sabzi, Shahab AU - Vajta, László ED - Kiss, Bálint ED - Szirmay-Kalos, László TI - Effects of electric vehicle charging stations on electricity grid: challenges and possible solutions T2 - Proceedings of the Workshop on the Advances of Information Technology (WAIT) 2021 PB - BME Irányítástechnika és Informatika Tanszék CY - Budapest SN - 9789634218449 PY - 2021 SP - 126 EP - 132 PG - 7 UR - https://m2.mtmt.hu/api/publication/33539071 ID - 33539071 LA - English DB - MTMT ER -