TY - JOUR AU - OULTILIGH, Ahmed AU - AYAD, Hassan AU - EL KARI, Abdeljalil AU - MJAHED, Mostafa AU - EL GMILI, Nada AU - Horváth, Ernő AU - POZNA, Claudiu TI - An Improved IEHO Super-Twisting Sliding Mode Control Algorithm for Trajectory Tracking of a Mobile Robot JF - STUDIES IN INFORMATICS AND CONTROL J2 - STUD INFORM CONTROL VL - 33 PY - 2024 IS - 1 SP - 49 EP - 60 PG - 12 SN - 1220-1766 DO - 10.24846/v33i1y202405 UR - https://m2.mtmt.hu/api/publication/34825488 ID - 34825488 LA - English DB - MTMT ER - TY - JOUR AU - Ignéczi, Gergő Ferenc AU - Horváth, Ernő AU - Tóth, Roland AU - Nyilas, K TI - Curve Trajectory Model for Human Preferred Path Planning of Automated Vehicles JF - Automotive Innovation J2 - Automot. Innov. VL - 7 PY - 2024 SP - 59 EP - 70 PG - 12 SN - 2096-4250 DO - 10.1007/s42154-023-00259-8 UR - https://m2.mtmt.hu/api/publication/34524770 ID - 34524770 N1 - Vehicle Research Center, Szechenyi Istvan University, Egyetem ter 1, Gyor, 9026, Hungary Institute for Computer Science and Control, Kende str. 13-17., Budapest, 1111, Hungary Robert Bosch Kft, Gyomroi str. 104-120, Budapest, 1103, Hungary Export Date: 5 February 2024 Correspondence Address: Igneczi, G.F.; Vehicle Research Center, Egyetem ter 1, Hungary; email: gergo.igneczi@ga.sze.hu AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Markó, Norbert AU - Horváth, Ernő AU - Szalay, István AU - Enisz, Krisztián TI - Deep Learning-Based Approach for Autonomous Vehicle Localization: Application and Experimental Analysis JF - MACHINES J2 - MACHINES VL - 11 PY - 2023 IS - 12 SN - 2075-1702 DO - 10.3390/machines11121079 UR - https://m2.mtmt.hu/api/publication/34474777 ID - 34474777 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Krecht, Rudolf AU - Budai, Tamás AU - Horváth, Ernő AU - Kovács, Ákos AU - Markó, Norbert AU - Unger, Miklós TI - Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions JF - IEEE NETWORK J2 - IEEE NETWORK VL - 37 PY - 2023 IS - 4 SP - 282 EP - 288 PG - 7 SN - 0890-8044 DO - 10.1109/MNET.007.2300023 UR - https://m2.mtmt.hu/api/publication/34223258 ID - 34223258 LA - English DB - MTMT ER - TY - CHAP AU - Ignéczi, Gergő Ferenc AU - Horváth, Ernő TI - Node Point Optimization for Local Trajectory Planners based on Human Preferences T2 - IEEE 21st World Symposium on Applied Machine Intelligence and Informatics SAMI (2023) : Proceedings PB - IEEE CY - Herlany SN - 9798350319859 PY - 2023 SP - 000225 EP - 000230 PG - 6 DO - 10.1109/SAMI58000.2023.10044488 UR - https://m2.mtmt.hu/api/publication/33676708 ID - 33676708 LA - English DB - MTMT ER - TY - CONF AU - Ghaith, Naddari AU - Koren, Csaba AU - Borsos, Attila AU - Horváth, Ernő TI - Evaluation of the quality of pavement markings using mobile LIDARs T2 - 34th ICTCT Conference: Road safety improvement tools - do we really know their impacts? PY - 2022 SP - 1 UR - https://m2.mtmt.hu/api/publication/33199279 ID - 33199279 LA - English DB - MTMT ER - TY - CHAP AU - Ignéczi, Gergő Ferenc AU - Horváth, Ernő TI - A Clothoid-based Local Trajectory Planner with Extended Kalman Filter T2 - IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics SAMI (2022) PB - IEEE CY - Poprad SN - 9781665497039 PY - 2022 SP - 467 EP - 472 PG - 6 DO - 10.1109/SAMI54271.2022.9780857 UR - https://m2.mtmt.hu/api/publication/32870592 ID - 32870592 LA - English DB - MTMT ER - TY - JOUR AU - Pozna, Claudiu Radu AU - Precup, Radu-Emil AU - Horváth, Ernő AU - Petriu, Emil M. TI - Hybrid Particle Filter-Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems JF - IEEE TRANSACTIONS ON FUZZY SYSTEMS J2 - IEEE T FUZZY SYST VL - 30 PY - 2022 IS - 10 SP - 4286 EP - 4297 PG - 14 SN - 1063-6706 DO - 10.1109/TFUZZ.2022.3146986 UR - https://m2.mtmt.hu/api/publication/32640087 ID - 32640087 LA - English DB - MTMT ER - TY - THES AU - Horváth, Ernő TI - Research and optimization of perception and trajectory planning algorithms for autonomous mobile robots and road vehicles PY - 2021 DO - 10.15477/SZE.MMTDI.2021.006 UR - https://m2.mtmt.hu/api/publication/33915156 ID - 33915156 LA - English DB - MTMT ER - TY - CHAP AU - Ignéczi, Gergő Ferenc AU - Horváth, Ernő ED - Szauter, Ferenc ED - Csikor, Dániel ED - Földesi, Rita ED - Suta, Alex TI - Klotoid alapú lokális trajektória tervező kibővített Kálmán-szűrővel T2 - AUTONÓM JÁRMŰVEK - Jövőformáló járműipari kutatások Konferenciakiadvány PB - Széchenyi István Egyetem CY - Győr SN - 9786156443137 PY - 2021 SP - 33 EP - 49 PG - 17 UR - https://m2.mtmt.hu/api/publication/33606958 ID - 33606958 LA - Hungarian DB - MTMT ER -