TY - JOUR AU - Szabó, Ádám AU - Doba, Dániel Károly AU - Aradi, Szilárd AU - Kiss, Péter TI - Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach JF - AGRICULTURE-BASEL J2 - AGRICULTURE-BASEL VL - 14 PY - 2024 IS - 3 PG - 16 SN - 2077-0472 DO - 10.3390/agriculture14030499 UR - https://m2.mtmt.hu/api/publication/34756334 ID - 34756334 N1 - Department of Control for Transportation and Vehicle Systems, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest, H-1111, Hungary Institute of Technology, Department of Vehicle Technology, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1., Gödöllő, H-2100, Hungary Export Date: 5 April 2024 Correspondence Address: Aradi, S.; Department of Control for Transportation and Vehicle Systems, Műegyetem rkp. 3., Hungary; email: aradi.szilard@kjk.bme.hu AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Aradi, Szilárd AU - Lindenmaier, László AU - Lövétei, István Ferenc ED - Szakál, Anikó TI - Runtime Performance Analysis of a MILP-Based Real-Time Railway Traffic Management Algorithm T2 - SISY 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics PB - IEEE Hungary Section CY - Budapest SN - 9798350343366 PY - 2023 SP - 000265 EP - 000270 PG - 6 DO - 10.1109/SISY60376.2023.10417744 UR - https://m2.mtmt.hu/api/publication/34568718 ID - 34568718 N1 - IEEE CI Chapter; IEEE CS Chapter; IEEE Hungary Section; IEEE IES; IEEE SMC Chapter; RAS Joint Chapter Conference code: 197114 Export Date: 8 March 2024 Correspondence Address: Aradi, S.; University of Technology and Economics, Hungary; email: aradi.szilard@kjk.bme.hu Funding details: RRF-2.3.1-21-2022-00002 Funding details: European Commission, EC Funding details: Magyar Tudományos Akadémia, MTA Funding text 1: This work was partially supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. Funding text 2: The research was supported in part by the European Union within the framework of the National Laboratory for Autonomous Systems. (RRF-2.3.1-21-2022-00002). AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Lindenmaier, László AU - Czibere, Balázs AU - Aradi, Szilárd AU - Bécsi, Tamás ED - Szakál, Anikó TI - A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems T2 - IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings PB - IEEE Hungary Section CY - Budapest SN - 9798350321104 PY - 2023 SP - 000785 EP - 000792 PG - 8 DO - 10.1109/SACI58269.2023.10158542 UR - https://m2.mtmt.hu/api/publication/34039486 ID - 34039486 N1 - Correspondence Address: Lindenmaier, L.; Budapest University of Technology and Economics, Hungary; email: lindenmaier.laszlo@kjk.bme.hu LA - English DB - MTMT ER - TY - CHAP AU - Kővári, Bálint AU - Balogh, Csanád L. AU - Aradi, Szilárd ED - Szakál, Anikó TI - Multi-Agent Reinforcement Learning for railway rescheduling T2 - IEEE 17th International Symposium on Applied Computational Intelligence and Informatics SACI 2023 : Proceedings PB - IEEE Hungary Section CY - Budapest SN - 9798350321104 PY - 2023 SP - 375 EP - 380 PG - 6 DO - 10.1109/SACI58269.2023.10158653 UR - https://m2.mtmt.hu/api/publication/34039481 ID - 34039481 N1 - Correspondence Address: Kovari, B.; Budapest University of Technology and Economics, Hungary; email: kovari.balint@kjk.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Lindenmaier, László AU - Aradi, Szilárd AU - Bécsi, Tamás AU - Törő, Olivér AU - Gáspár, Péter TI - Object-Level Data-Driven Sensor Simulation for Automotive Environment Perception JF - IEEE Transactions on Intelligent Vehicles J2 - IEEE Transactions on Intelligent Vehicles VL - 8 PY - 2023 IS - 10 SP - 4341 EP - 4356 PG - 15 SN - 2379-8858 DO - 10.1109/TIV.2023.3287278 UR - https://m2.mtmt.hu/api/publication/34027317 ID - 34027317 N1 - Budapest University of Technology and Economics, Department of Control for Transportation and Vehicle Systems, Budapest, 1111, Hungary Institute for Computer Science and Control, Systems and Control Laboratory, Budapest, 1111, Hungary Export Date: 12 December 2023 Correspondence Address: Bécsi, T.; Budapest University of Technology and Economics, Hungary; email: becsi.tamas@kjk.bme.hu LA - English DB - MTMT ER - TY - JOUR AU - Szőke, László AU - Aradi, Szilárd AU - Bécsi, Tamás TI - Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent JF - ELECTRONICS (SWITZ) VL - 12 PY - 2023 IS - 6 PG - 14 SN - 2079-9292 DO - 10.3390/electronics12061442 UR - https://m2.mtmt.hu/api/publication/33706849 ID - 33706849 AB - 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. LA - English DB - MTMT ER - TY - JOUR AU - Kolat, Máté AU - Kővári, Bálint AU - Bécsi, Tamás AU - Aradi, Szilárd TI - Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach JF - SUSTAINABILITY J2 - SUSTAINABILITY-BASEL VL - 15 PY - 2023 IS - 4 PG - 13 SN - 2071-1050 DO - 10.3390/su15043479 UR - https://m2.mtmt.hu/api/publication/33644779 ID - 33644779 AB - 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. LA - English DB - MTMT ER - TY - CONF AU - Kővári, Bálint AU - Lövétei, István Ferenc AU - Aradi, Szilárd AU - Bécsi, Tamás TI - Multi-Agent Deep Reinforcement Learning (MADRL) for Solving Real-Time Railway Rescheduling Problem T2 - Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance PB - Civil-Comp Press C1 - Edinburgh T3 - Civil-Comp Conferences, ISSN 2753-3239 ; 1. PY - 2022 PG - 6 DO - 10.4203/ccc.1.23.5 UR - https://m2.mtmt.hu/api/publication/34041284 ID - 34041284 LA - English DB - MTMT ER - TY - CHAP AU - Farkas, Péter AU - Szőke, László AU - Aradi, Szilárd TI - Defining metrics for scenario-based evaluation of autonomous vehicle models T2 - 2022 IEEE 1st International Conference on Cognitive Mobility (CogMob) PB - IEEE CY - Piscataway (NJ) SN - 9781665476324 PY - 2022 SP - 155 EP - 160 PG - 6 DO - 10.1109/CogMob55547.2022.10117768 UR - https://m2.mtmt.hu/api/publication/34004114 ID - 34004114 N1 - Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Department of Control for Transportation and Vehicle Systems, Hungary Robert Bosch Kft., Budapest, Hungary Export Date: 8 June 2023 LA - English DB - MTMT ER - TY - CHAP AU - Kolat, Máté AU - Kővári, Bálint AU - Bécsi, Tamás AU - Aradi, Szilárd ED - Szakál, Anikó TI - Designing Reward Functions in Multi-Agent Reinforcement Learning for Intelligent Intersection Control T2 - IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY 2022) PB - IEEE CY - Szabadka SN - 9781665489881 PY - 2022 SP - 343 EP - 348 PG - 6 DO - 10.1109/SISY56759.2022.10036253 UR - https://m2.mtmt.hu/api/publication/33665513 ID - 33665513 LA - English DB - MTMT ER -