@article{MTMT:34417880,
	title = {Forecasting critical weather front transitions based on locally measured meteorological data},
	url = {https://m2.mtmt.hu/api/publication/34417880},
	author = {Szántó, Mátyás and Vajta, László},
	doi = {10.28974/idojaras.2023.4.3},
	journal-iso = {IDŐJÁRÁS},
	journal = {IDŐJÁRÁS / QUARTERLY JOURNAL OF THE HUNGARIAN METEOROLOGICAL SERVICE},
	volume = {127},
	unique-id = {34417880},
	issn = {0324-6329},
	abstract = {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.},
	keywords = {machine learning; crowdsourcing; local weather and weather fronts; weather front prediction},
	year = {2023},
	eissn = {0324-6329},
	pages = {459-471},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@inproceedings{MTMT:34094269,
	title = {Energy Management Strategy based Charging Coordination for Electric Vehicle Integrated Distribution Grid},
	url = {https://m2.mtmt.hu/api/publication/34094269},
	author = {Faghihi, T. and Sabzi, Shahab and Vajta, László},
	booktitle = {2023 International Conference on Future Energy Solutions (FES)},
	doi = {10.1109/FES57669.2023.10182883},
	unique-id = {34094269},
	year = {2023},
	pages = {1-6},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@article{MTMT:34069746,
	title = {Trajectory Planning of Automated Vehicles Using Real-Time Map Updates},
	url = {https://m2.mtmt.hu/api/publication/34069746},
	author = {Szántó, Mátyás and Hidalgo, C. and Gonzalez, L. and Perez, J. and Asua, E. and Vajta, László},
	doi = {10.1109/ACCESS.2023.3291350},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {11},
	unique-id = {34069746},
	issn = {2169-3536},
	year = {2023},
	eissn = {2169-3536},
	pages = {67468-67481},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@inproceedings{MTMT:33695726,
	title = {Relationship and Differences Between Entrepreneurship and Research in the CrowdMapping Project for Crowdsourced Urban Data},
	url = {https://m2.mtmt.hu/api/publication/33695726},
	author = {Szántó, Mátyás and Vajta, László},
	booktitle = {Smart Energy for Smart Transport},
	doi = {10.1007/978-3-031-23721-8_44},
	unique-id = {33695726},
	year = {2023},
	pages = {531-541},
	orcid-numbers = {Szántó, Mátyás/0000-0003-1793-147X; Vajta, László/0000-0001-7164-6050}
}

@article{MTMT:33609760,
	title = {Building Maps Using Monocular Image-feeds from Windshield-mounted Cameras in a Simulator Environment},
	url = {https://m2.mtmt.hu/api/publication/33609760},
	author = {Szántó, Mátyás and Kobál, Sándor and Vajta, László and Horváth, Viktor Győző and Lógó, János Máté and Barsi, Árpád},
	doi = {10.3311/PPci.21500},
	journal-iso = {PERIOD POLYTECH CIV ENG},
	journal = {PERIODICA POLYTECHNICA-CIVIL ENGINEERING},
	volume = {67},
	unique-id = {33609760},
	issn = {0553-6626},
	abstract = {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.},
	keywords = {sensors; crowdsourcing; Environmental reconstruction; SfM; Structure-from-motion; automotive simulator},
	year = {2023},
	eissn = {1587-3773},
	pages = {457-472},
	orcid-numbers = {Szántó, Mátyás/0000-0003-1793-147X; Vajta, László/0000-0001-7164-6050; Horváth, Viktor Győző/0000-0002-4058-2768; Lógó, János Máté/0000-0002-0946-5328; Barsi, Árpád/0000-0002-0298-7502}
}

@article{MTMT:32839439,
	title = {Security and Energy Consumption Considerations of Electric Vehicles Integration in Smart Grids},
	url = {https://m2.mtmt.hu/api/publication/32839439},
	author = {Sabzi, Shahab and Vajta, László},
	doi = {10.24840/2183-6493_009-001_001382},
	journal-iso = {UPjeng},
	journal = {U.Porto Journal of Engineering},
	volume = {9},
	unique-id = {32839439},
	year = {2023},
	eissn = {2183-6493},
	pages = {134-149},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@inproceedings{MTMT:33538477,
	title = {Effects of electric vehicles and PV units on the distribution network, a modified IEEE 31 buses distribution network case study},
	url = {https://m2.mtmt.hu/api/publication/33538477},
	author = {Tayebeh, Faghihi and Sabzi, Shahab and Vajta, László},
	booktitle = {Proceedings of the Workshop on the Advances in Information Technology 2022},
	unique-id = {33538477},
	year = {2022},
	pages = {151-156},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@article{MTMT:32937203,
	title = {ATDN vSLAM: An All-Through Deep Learning-Based Solution for Visual Simultaneous Localization and Mapping},
	url = {https://m2.mtmt.hu/api/publication/32937203},
	author = {Szántó, Mátyás and Bogár, György Richárd and Vajta, László},
	doi = {10.3311/PPee.20437},
	journal-iso = {PERIOD POLYTECH ELECTR ENG COMP SCI},
	journal = {PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE},
	volume = {66},
	unique-id = {32937203},
	issn = {2064-5260},
	abstract = {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.},
	year = {2022},
	eissn = {2064-5279},
	pages = {236-247},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@article{MTMT:32818781,
	title = {A Review on Electric Vehicles Charging Strategies Concerning Actors Interests},
	url = {https://m2.mtmt.hu/api/publication/32818781},
	author = {Sabzi, Shahab and Vajta, László and Faghihi, Tayebeh},
	doi = {10.3311/PPee.19625},
	journal-iso = {PERIOD POLYTECH ELECTR ENG COMP SCI},
	journal = {PERIODICA POLYTECHNICA-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE},
	volume = {66},
	unique-id = {32818781},
	issn = {2064-5260},
	abstract = {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.},
	year = {2022},
	eissn = {2064-5279},
	pages = {148-162},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}

@inproceedings{MTMT:33539071,
	title = {Effects of electric vehicle charging stations on electricity grid: challenges and possible solutions},
	url = {https://m2.mtmt.hu/api/publication/33539071},
	author = {Sabzi, Shahab and Vajta, László},
	booktitle = {Proceedings of the Workshop on the Advances of Information Technology (WAIT) 2021},
	unique-id = {33539071},
	year = {2021},
	pages = {126-132},
	orcid-numbers = {Vajta, László/0000-0001-7164-6050}
}