@article{MTMT:34756334, title = {Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach}, url = {https://m2.mtmt.hu/api/publication/34756334}, author = {Szabó, Ádám and Doba, Dániel Károly and Aradi, Szilárd and Kiss, Péter}, doi = {10.3390/agriculture14030499}, journal-iso = {AGRICULTURE-BASEL}, journal = {AGRICULTURE-BASEL}, volume = {14}, unique-id = {34756334}, abstract = {The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability.}, year = {2024}, eissn = {2077-0472}, orcid-numbers = {Kiss, Péter/0000-0002-5265-8133} } @inproceedings{MTMT:34568718, title = {Runtime Performance Analysis of a MILP-Based Real-Time Railway Traffic Management Algorithm}, url = {https://m2.mtmt.hu/api/publication/34568718}, author = {Aradi, Szilárd and Lindenmaier, László and Lövétei, István Ferenc}, booktitle = {SISY 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics}, doi = {10.1109/SISY60376.2023.10417744}, unique-id = {34568718}, abstract = {The real-Time railway traffic management problem occurs when the trains get off schedule due to different traffic perturbations. In this case, they must be rerouted, reordered, and rescheduled to resolve the possible conflicts. Nowadays, this problem is usually handled by human dispatchers. There are lots of algorithms aiming to support human dispatchers in making an optimal decision that minimizes delays. However, due to the real-Time nature of the problem, the response time of these algorithms is crucial. In this paper, the runtime performance of a state-of-The-Art mixed-integer linear programming model is analyzed in different solvers. The analysis is performed via Monte Carlo simulation, generating various realistic scenarios in an infrastructure model of a Hungarian railway control area. © 2023 IEEE.}, keywords = {Optimization; intelligent systems; Monte Carlo methods; Linear programming; Integer programming; Optimisations; Runtimes; Railroads; Performance analysis; Runtime; Railroad transportation; Railway traffic management; rail transportation; Rails; Real- time; railway safety; Management problems; Performances analysis; Linear-programming; Runtime performance}, year = {2023}, pages = {000265-000270} } @inproceedings{MTMT:34039486, title = {A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems}, url = {https://m2.mtmt.hu/api/publication/34039486}, author = {Lindenmaier, László and Czibere, Balázs and Aradi, Szilárd and Bécsi, Tamás}, booktitle = {IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings}, doi = {10.1109/SACI58269.2023.10158542}, unique-id = {34039486}, year = {2023}, pages = {000785-000792}, orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672} } @inproceedings{MTMT:34039481, title = {Multi-Agent Reinforcement Learning for railway rescheduling}, url = {https://m2.mtmt.hu/api/publication/34039481}, author = {Kővári, Bálint and Balogh, Csanád L. and Aradi, Szilárd}, booktitle = {IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings}, doi = {10.1109/SACI58269.2023.10158653}, unique-id = {34039481}, year = {2023}, pages = {375-380} } @article{MTMT:34027317, title = {Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception}, url = {https://m2.mtmt.hu/api/publication/34027317}, author = {Lindenmaier, László and Aradi, Szilárd and Bécsi, Tamás and Törő, Olivér and Gáspár, Péter}, doi = {10.1109/TIV.2023.3287278}, journal-iso = {IEEE Transactions on Intelligent Vehicles}, journal = {IEEE Transactions on Intelligent Vehicles}, volume = {8}, unique-id = {34027317}, issn = {2379-8858}, year = {2023}, eissn = {2379-8904}, pages = {4341-4356}, orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672; Törő, Olivér/0000-0002-7288-5229; Gáspár, Péter/0000-0003-3388-1724} } @article{MTMT:33706849, title = {Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent}, url = {https://m2.mtmt.hu/api/publication/33706849}, author = {Szőke, László and Aradi, Szilárd and Bécsi, Tamás}, doi = {10.3390/electronics12061442}, journal = {ELECTRONICS (SWITZ)}, volume = {12}, unique-id = {33706849}, abstract = {In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network capacity (throughput). Recent studies embrace machine learning approaches that have the power to aid and optimize the increasing demands. However, most reinforcement learning algorithms fail to be adaptive regarding goal functions. To this end, we provide a novel successor feature-based solution to control a single intersection to optimize the traffic flow, reduce the environmental impact, and promote sustainability. Our method allows for flexibility and adaptability to changing circumstances and goals. It supports changes in preferences during inference, so the behavior of the trained agent (traffic signal controller) can be changed rapidly during the inference time. By introducing the successor features to the domain, we define the basics of successor features, the base reward functions, and the goal preferences of the traffic signal control system. As our main direction, we tackle environmental impact reduction and support prioritized vehicles’ commutes. We include an evaluation of how our method achieves a more effective operation considering the environmental impact and how adaptive it is compared to a general Deep-Q-Network solution. Aside from this, standard rule-based and adaptive signal-controlling technologies are compared to our method to show its advances. Furthermore, we perform an ablation analysis on the adaptivity of the agent and demonstrate a consistent level of performance under similar circumstances.}, year = {2023}, eissn = {2079-9292}, orcid-numbers = {Szőke, László/0000-0001-9926-4054; Bécsi, Tamás/0000-0002-1487-9672} } @article{MTMT:33644779, title = {Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach}, url = {https://m2.mtmt.hu/api/publication/33644779}, author = {Kolat, Máté and Kővári, Bálint and Bécsi, Tamás and Aradi, Szilárd}, doi = {10.3390/su15043479}, journal-iso = {SUSTAINABILITY-BASEL}, journal = {SUSTAINABILITY}, volume = {15}, unique-id = {33644779}, abstract = {The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.}, year = {2023}, eissn = {2071-1050}, orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672} } @CONFERENCE{MTMT:34041284, title = {Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem}, url = {https://m2.mtmt.hu/api/publication/34041284}, author = {Kővári, Bálint and Lövétei, István Ferenc and Aradi, Szilárd and Bécsi, Tamás}, booktitle = {Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance}, doi = {10.4203/ccc.1.23.5}, unique-id = {34041284}, year = {2022}, orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672} } @inproceedings{MTMT:34004114, title = {Defining metrics for scenario-based evaluation of autonomous vehicle models}, url = {https://m2.mtmt.hu/api/publication/34004114}, author = {Farkas, Péter and Szőke, László and Aradi, Szilárd}, booktitle = {2022 IEEE 1st International Conference on Cognitive Mobility (CogMob)}, doi = {10.1109/CogMob55547.2022.10117768}, unique-id = {34004114}, year = {2022}, pages = {155-160}, orcid-numbers = {Szőke, László/0000-0001-9926-4054} } @inproceedings{MTMT:33665513, title = {Designing Reward Functions in Multi-Agent Reinforcement Learning for Intelligent Intersection Control}, url = {https://m2.mtmt.hu/api/publication/33665513}, author = {Kolat, Máté and Kővári, Bálint and Bécsi, Tamás and Aradi, Szilárd}, booktitle = {IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY 2022)}, doi = {10.1109/SISY56759.2022.10036253}, unique-id = {33665513}, year = {2022}, pages = {343-348}, orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672} }