| Survey of deep reinforcement learning for motion planning of autonomous vehicles S Aradi IEEE Transactions on Intelligent Transportation Systems 23 (2), 740-759, 2020 | 421 | 2020 |
| Security issues and vulnerabilities in connected car systems T Bécsi, S Aradi, P Gáspár 2015 International Conference on Models and Technologies for Intelligent …, 2015 | 92 | 2015 |
| Design of lane keeping algorithm of autonomous vehicle O Törő, T Becsi, S Aradi Periodica Polytechnica Transportation Engineering 44 (1), 60-68, 2016 | 71 | 2016 |
| Highly automated vehicle systems P Gáspár, Z Szalay, S Aradi BME MOGI, 2014 | 38 | 2014 |
| Model based trajectory planning for highly automated road vehicles F Hegedüs, T Bécsi, S Aradi, P Gápár IFAC-PapersOnLine 50 (1), 6958-6964, 2017 | 37 | 2017 |
| Policy gradient based reinforcement learning approach for autonomous highway driving S Aradi, T Becsi, P Gaspar 2018 IEEE Conference on Control Technology and Applications (CCTA), 670-675, 2018 | 31 | 2018 |
| Hierarchical evasive path planning using reinforcement learning and model predictive control Á Fehér, S Aradi, T Bécsi IEEE Access 8, 187470-187482, 2020 | 29 | 2020 |
| A predictive optimization method for energy-optimal speed profile generation for trains S Aradi, T Bécsi, P Gáspár 2013 IEEE 14th International Symposium on Computational Intelligence and …, 2013 | 28 | 2013 |
| Design of predictive optimization method for energy-efficient operation of trains S Aradi, T Bécsi, P Gáspár 2014 European Control Conference (ECC), 2490-2495, 2014 | 27* | 2014 |
| Hybrid DDPG approach for vehicle motion planning Á Fehér, S Aradi, F Hegedüs, T Bécsi, P Gáspár SciTePress, 2019 | 24 | 2019 |
| Lane change prediction using Gaussian classification, support vector classification and neural network classifiers O Rákos, S Aradi, T Bécsi Periodica Polytechnica Transportation Engineering 48 (4), 327-333, 2020 | 21 | 2020 |
| Highly automated vehicle systems G Péter, S Zsolt, A Szilárd BME MOGI, 2014 | 19 | 2014 |
| Motion planning for highly automated road vehicles with a hybrid approach using nonlinear optimization and artificial neural networks F Hegedüs, T Bécsi, S Aradi, P Gáspár Strojniski vestnik-Journal of Mechanical Engineering 65 (3), 148-160, 2019 | 18 | 2019 |
| Q-learning based reinforcement learning approach for lane keeping A Feher, S Aradi, T Becsi 2018 IEEE 18th International Symposium on Computational Intelligence and …, 2018 | 17 | 2018 |
| Traffic signal control via reinforcement learning for reducing global vehicle emission B Kővári, L Szőke, T Bécsi, S Aradi, P Gáspár Sustainability 13 (20), 11254, 2021 | 14 | 2021 |
| Control oriented modeling of an electro-pneumatic gearbox actuator A Szabo, T Becsi, P Gaspar, S Aradi 2018 European Control Conference (ECC), 2623-2628, 2018 | 13 | 2018 |
| Multi-agent reinforcement learning for traffic signal control: A cooperative approach M Kolat, B Kővári, T Bécsi, S Aradi Sustainability 15 (4), 3479, 2023 | 12 | 2023 |
| Reinforcement learning based control design for a floating piston pneumatic gearbox actuator T Bécsi, Á Szabó, B Kővári, S Aradi, P Gáspár IEEE Access 8, 147295-147312, 2020 | 12 | 2020 |
| Highway environment model for reinforcement learning T Bécsi, S Aradi, Á Fehér, J Szalay, P Gáspár IFAC-PapersOnLine 51 (22), 429-434, 2018 | 12 | 2018 |
| Reward design for intelligent intersection control to reduce emission B Kővári, B Pelenczei, S Aradi, T Bécsi IEEE Access 10, 39691-39699, 2022 | 11 | 2022 |
