@article{MTMT:34825488,
	title = {An Improved IEHO Super-Twisting Sliding Mode Control Algorithm for Trajectory Tracking of a Mobile Robot},
	url = {https://m2.mtmt.hu/api/publication/34825488},
	author = {OULTILIGH, Ahmed and AYAD, Hassan and EL KARI, Abdeljalil and MJAHED, Mostafa and EL GMILI, Nada and Horváth, Ernő and POZNA, Claudiu},
	doi = {10.24846/v33i1y202405},
	journal-iso = {STUD INFORM CONTROL},
	journal = {STUDIES IN INFORMATICS AND CONTROL},
	volume = {33},
	unique-id = {34825488},
	issn = {1220-1766},
	year = {2024},
	eissn = {1841-429X},
	pages = {49-60},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:34524770,
	title = {Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles},
	url = {https://m2.mtmt.hu/api/publication/34524770},
	author = {Ignéczi, Gergő Ferenc and Horváth, Ernő and Tóth, Roland and Nyilas, K},
	doi = {10.1007/s42154-023-00259-8},
	journal-iso = {Automot. Innov.},
	journal = {Automotive Innovation},
	volume = {7},
	unique-id = {34524770},
	issn = {2096-4250},
	abstract = {Automated driving systems are often used for lane keeping tasks. By these systems, a local path is planned ahead of the vehicle. However, these paths are often found unnatural by human drivers. In response to this, this paper proposes a linear driver model, which can calculate node points reflective of human driver preferences and based on these node points a human driver preferred motion path can be designed for autonomous driving. The model input is the road curvature, effectively harnessed through a self-developed Euler-curve-based curve fitting algorithm. A comprehensive case study is undertaken to empirically validate the efficacy of the proposed model, demonstrating its capacity to emulate the average behavioral patterns observed in human curve path selection. Statistical analyses further underscore the model's robustness, affirming the authenticity of the established relationships. This paradigm shift in trajectory planning holds promising implications for the seamless integration of autonomous driving systems with human driving preferences.},
	year = {2024},
	eissn = {2522-8765},
	pages = {59-70},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:34474777,
	title = {Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis},
	url = {https://m2.mtmt.hu/api/publication/34474777},
	author = {Markó, Norbert and Horváth, Ernő and Szalay, István and Enisz, Krisztián},
	doi = {10.3390/machines11121079},
	journal-iso = {MACHINES},
	journal = {MACHINES},
	volume = {11},
	unique-id = {34474777},
	abstract = {In a vehicle, wheel speed sensors and inertial measurement units (IMUs) are present onboard, and their raw data can be used for localization estimation. Both wheel sensors and IMUs encounter challenges such as bias and measurement noise, which accumulate as errors over time. Even a slight inaccuracy or minor error can render the localization system unreliable and unusable in a matter of seconds. Traditional algorithms, such as the extended Kalman filter (EKF), have been applied for a long time in non-linear systems. These systems have white noise in both the system and in the estimation model. These approaches require deep knowledge of the non-linear noise characteristics of the sensors. On the other hand, as a subset of artificial intelligence (AI), neural network-based (NN) algorithms do not necessarily have these strict requirements. The current paper proposes an AI-based long short-term memory (LSTM) localization approach and evaluates its performance against the ground truth.},
	year = {2023},
	eissn = {2075-1702},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:34223258,
	title = {Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions},
	url = {https://m2.mtmt.hu/api/publication/34223258},
	author = {Krecht, Rudolf and Budai, Tamás and Horváth, Ernő and Kovács, Ákos and Markó, Norbert and Unger, Miklós},
	doi = {10.1109/MNET.007.2300023},
	journal-iso = {IEEE NETWORK},
	journal = {IEEE NETWORK},
	volume = {37},
	unique-id = {34223258},
	issn = {0890-8044},
	year = {2023},
	eissn = {1558-156X},
	pages = {282-288},
	orcid-numbers = {Krecht, Rudolf/0000-0002-8927-8783; Horváth, Ernő/0000-0001-5083-2073}
}

@inproceedings{MTMT:33676708,
	title = {Node Point Optimization for Local Trajectory Planners based on Human Preferences},
	url = {https://m2.mtmt.hu/api/publication/33676708},
	author = {Ignéczi, Gergő Ferenc and Horváth, Ernő},
	booktitle = {IEEE 21st World Symposium on Applied Machine Intelligence and Informatics SAMI (2023) : Proceedings},
	doi = {10.1109/SAMI58000.2023.10044488},
	unique-id = {33676708},
	year = {2023},
	pages = {000225-000230},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@CONFERENCE{MTMT:33199279,
	title = {Evaluation of the quality of pavement markings using mobile LIDARs},
	url = {https://m2.mtmt.hu/api/publication/33199279},
	author = {Ghaith, Naddari and Koren, Csaba and Borsos, Attila and Horváth, Ernő},
	booktitle = {34th ICTCT Conference: Road safety improvement tools - do we really know their impacts?},
	unique-id = {33199279},
	year = {2022},
	pages = {1},
	orcid-numbers = {Koren, Csaba/0000-0002-1034-0557; Horváth, Ernő/0000-0001-5083-2073}
}

@inproceedings{MTMT:32870592,
	title = {A Clothoid-based Local Trajectory Planner with Extended Kalman Filter},
	url = {https://m2.mtmt.hu/api/publication/32870592},
	author = {Ignéczi, Gergő Ferenc and Horváth, Ernő},
	booktitle = {IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics SAMI (2022)},
	doi = {10.1109/SAMI54271.2022.9780857},
	unique-id = {32870592},
	year = {2022},
	pages = {467-472},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@article{MTMT:32640087,
	title = {Hybrid Particle Filter-Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems},
	url = {https://m2.mtmt.hu/api/publication/32640087},
	author = {Pozna, Claudiu Radu and Precup, Radu-Emil and Horváth, Ernő and Petriu, Emil M.},
	doi = {10.1109/TFUZZ.2022.3146986},
	journal-iso = {IEEE T FUZZY SYST},
	journal = {IEEE TRANSACTIONS ON FUZZY SYSTEMS},
	volume = {30},
	unique-id = {32640087},
	issn = {1063-6706},
	year = {2022},
	eissn = {1941-0034},
	pages = {4286-4297},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@mastersthesis{MTMT:33915156,
	title = {Research and optimization of perception and trajectory planning algorithms for autonomous mobile robots and road vehicles},
	url = {https://m2.mtmt.hu/api/publication/33915156},
	author = {Horváth, Ernő},
	doi = {10.15477/SZE.MMTDI.2021.006},
	unique-id = {33915156},
	year = {2021},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}

@inproceedings{MTMT:33606958,
	title = {Klotoid alapú lokális trajektória tervező kibővített Kálmán-szűrővel},
	url = {https://m2.mtmt.hu/api/publication/33606958},
	author = {Ignéczi, Gergő Ferenc and Horváth, Ernő},
	booktitle = {AUTONÓM JÁRMŰVEK - Jövőformáló járműipari kutatások Konferenciakiadvány},
	unique-id = {33606958},
	year = {2021},
	pages = {33-49},
	orcid-numbers = {Horváth, Ernő/0000-0001-5083-2073}
}