Dr. rer. nat. Evgeny Kusmenko

page.title
Software Engineering
Department of Computer Science 3
RWTH Aachen University
Ahornstraße 55
D-52074 Aachen

+49 (241) 80-21342
kusmenko@se-rwth.de

Room 4220


Field of Work:

  • Automotive
  • Autonomous Vehicles
  • Machine Learning and Data Science
  • Robotics
  • Software Architectures

Publications:

  1. [KKK+24]
    C. Kehl, M. Kloock, E. Kusmenko, L. Eckstein, B. Alrifaee, S. Kowalewski, B. Rumpe:
    AutoKnigge—Modeling, Evaluation and Verification of Cooperative Interacting Automobiles.
    In: Cooperatively Interacting Vehicles: Methods and Effects of Automated Cooperation in Traffic, C. Stiller, M. Althoff, C. Burger, B. Deml, L. Eckstein, F. Flemisch (Eds.), pp. 347–412, ISBN 978-3-031-60494-2, Springer International Publishing, Cham, Aug. 2024.
  2. [HHKR23]
    T. Höfer, M. Hoppe, E. Kusmenko, B. Rumpe:
    MontiSim: Agent-Based Simulation for Reinforcement Learning of Autonomous Driving.
    In: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), pp. 2634-2639, IEEE, Sep. 2023.
  3. [BKK+23]
    V. Bertram, H. Kausch, E. Kusmenko, H. Nqiri, B. Rumpe, C. Venhoff:
    Leveraging Natural Language Processing for a Consistency Checking Toolchain of Automotive Requirements.
    In: IEEE 31st International Conference on Requirements Engineering (RE), K. Schneider, F. Dalpiaz, J. Horkoff (Eds.), pp. 212-222, ACM/IEEE, Sep. 2023.
  4. [KMNR22]
    E. Kusmenko, M. Münker, M. Nadenau, B. Rumpe:
    A Model-Driven Generative Self Play-Based Toolchain for Developing Games and Players.
    In: Proceedings of the 21st ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 22), pp. 95-107, ACM, Dec. 2022.
  5. [BBK+22b]
    V. Bertram, M. Boß, E. Kusmenko, I. Nachmann, B. Rumpe, D. Trotta, L. Wachtmeister:
    Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE.
    In: International Conference on Software Language Engineering (SLE’22), pp. 260-265, ACM, Dec. 2022.
  6. [BBK+22a]
    V. Bertram, M. Boß, E. Kusmenko, I. H. Nachmann, B. Rumpe, D. Trotta, L. Wachtmeister:
    Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE.
    Art. 2211.09084, arXiv, Nov. 2022.
  7. [BKR+22]
    N. Baumann, E. Kusmenko, J. Ritz, B. Rumpe, M. Weber:
    Dynamic Data Management for Continuous Retraining.
    In: Proceedings of MODELS 2022. Workshop MDE Intelligence, pp. 359 - 366, ACM, Oct. 2022.
  8. [KKR+22a]
    J. C. Kirchhof, E. Kusmenko, J. Ritz, B. Rumpe, A. Moin, A. Badii, S. Günnemann, M. Challenger:
    MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat.
    In: Proceedings of MODELS 2022. Workshop MDE Intelligence, pp. 380-387, ACM, Oct. 2022.
  9. [HKK+22]
    M. Hoppe, J. C. Kirchhof, E. Kusmenko, C. Y. Lee, B. Rumpe:
    Agent-Based Autonomous Vehicle Simulation with Hardware Emulation in the Loop.
    In: 2022 IEEE Intelligent Vehicles Symposium (IV’22), pp. 16-21, IEEE, Jun. 2022.
  10. [Kus21]
    E. Kusmenko:
    Model-Driven Development Methodology and Domain-Specific Languages for the Design of Artificial Intelligence in Cyber-Physical Systems.
    Aachener Informatik-Berichte, Software Engineering, Band 49, ISBN 978-3-8440-8286-9, Shaker Verlag, Nov. 2021.
  11. [HKR21a]
    M. Heithoff, E. Kusmenko, B. Rumpe:
    Synchronous Execution Semantics for Component & Connector Models.
    In: Companion Proceedings of MODELS 2021. Workshop MoDeVVa, S. Bin Abid, R. Oliveira, I. Ober (Eds.), pp. 264-273, ACM/IEEE, Oct. 2021.
  12. [AKK+21]
    A. Atouani, J. C. Kirchhof, E. Kusmenko, B. Rumpe:
    Artifact and Reference Models for Generative Machine Learning Frameworks and Build Systems.
    In: Proceedings of the 20th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences (GPCE 21), E. Tilevich, C. De Roover (Eds.), pp. 55-68, ACM, Oct. 2021.
  13. [KPRS19]
    E. Kusmenko, S. Pavlitskaya, B. Rumpe, S. Stüber:
    On the Engineering of AI-Powered Systems.
    In: ASE19. Software Engineering Intelligence Workshop (SEI19), L. O’Conner (Eds.), pp. 126-133, IEEE, Nov. 2019.
  14. [KNP+19]
    E. Kusmenko, S. Nickels, S. Pavlitskaya, B. Rumpe, T. Timmermanns:
    Modeling and Training of Neural Processing Systems.
    In: Conference on Model Driven Engineering Languages and Systems (MODELS’19), M. Kessentini, T. Yue, A. Pretschner, S. Voss, L. Burgueño (Eds.), pp. 283-293, IEEE, Sep. 2019.
  15. [GKR19]
    N. Gatto, E. Kusmenko, B. Rumpe:
    Modeling Deep Reinforcement Learning Based Architectures for Cyber-Physical Systems.
    In: Proceedings of MODELS 2019. Workshop MDE Intelligence, L. Burgueño, A. Pretschner, S. Voss, M. Chaudron, J. Kienzle, M. Völter, S. Gérard, M. Zahedi, E. Bousse, A. Rensink, F. Polack, G. Engels, G. Kappel (Eds.), pp. 196-202, Sep. 2019.
  16. [KKRZ19]
    J. C. Kirchhof, E. Kusmenko, B. Rumpe, H. Zhang:
    Simulation as a Service for Cooperative Vehicles.
    In: Proceedings of MODELS 2019. Workshop MASE, L. Burgueño, A. Pretschner, S. Voss, M. Chaudron, J. Kienzle, M. Völter, S. Gérard, M. Zahedi, E. Bousse, A. Rensink, F. Polack, G. Engels, G. Kappel (Eds.), pp. 28-37, IEEE, Sep. 2019.
  17. [KKMR19]
    J. C. Kirchhof, E. Kusmenko, J. Meurice, B. Rumpe:
    Simulation of Model Execution for Embedded Systems.
    In: Proceedings of MODELS 2019. Workshop MLE, L. Burgueño, A. Pretschner, S. Voss, M. Chaudron, J. Kienzle, M. Völter, S. Gérard, M. Zahedi, E. Bousse, A. Rensink, F. Polack, G. Engels, G. Kappel (Eds.), pp. 331-338, IEEE, Sep. 2019.
  18. [KKR19]
    N. Kaminski, E. Kusmenko, B. Rumpe:
    Modeling Dynamic Architectures of Self-Adaptive Cooperative Systems.
    In: Journal of Object Technology (JOT), B. Combemale, S. Ali (Eds.), Volume 18(2), pp. 1-20, AITO - Association Internationale pour les Technologies Objets, Jul. 2019.
  19. [HKKR19]
    A. Hellwig, S. Kriebel, E. Kusmenko, B. Rumpe:
    Component-based Integration of Interconnected Vehicle Architectures.
    In: 30th Intelligent Vehicles Symposium (IV’19). Workshop on Cooperative Interactive Vehicles, pp. 146-151, IEEE, Jun. 2019.
  20. [KKRS19]
    S. Kriebel, E. Kusmenko, B. Rumpe, I. Shumeiko:
    Learning Error Patterns from Diagnosis Trouble Codes.
    In: 30th Intelligent Vehicles Symposium (IV’19). Workshop on Unsupervised Learning for Automated Driving, pp. 246-251, IEEE, Jun. 2019.
  21. [DGH+19]
    I. Drave, T. Greifenberg, S. Hillemacher, S. Kriebel, E. Kusmenko, M. Markthaler, P. Orth, K. S. Salman, J. Richenhagen, B. Rumpe, C. Schulze, M. Wenckstern, A. Wortmann:
    SMArDT modeling for automotive software testing.
    In: Journal on Software: Practice and Experience, R. Buyya, J. Bishop, K. Cooper, R. Jonas, A. Poggi, S. Srirama (Eds.), Volume 49(2), pp. 301-328, Wiley Online Library, Feb. 2019.
  22. [KRS+18a]
    E. Kusmenko, B. Rumpe, S. Schneiders, M. von Wenckstern:
    Highly-Optimizing and Multi-Target Compiler for Embedded System Models: C++ Compiler Toolchain for the Component and Connector Language EmbeddedMontiArc.
    In: Conference on Model Driven Engineering Languages and Systems (MODELS’18), pp. 447 - 457, ACM, Oct. 2018.
  23. [KRR+18]
    E. Kusmenko, J.- M. Ronck, B. Rumpe, M. von Wenckstern:
    EmbeddedMontiArc: Textual Modeling Alternative to Simulink.
    In: Proceedings of MODELS 2018. Workshop EXE, Oct. 2018.
  24. [KRS+18b]
    E. Kusmenko, B. Rumpe, I. Strepkov, M. von Wenckstern:
    Teaching Playground for C&C Language EmbeddedMontiArc.
    In: Proceedings of MODELS 2018. Workshop ModComp, Oct. 2018.
  25. [BKL+18]
    C. Brecher, E. Kusmenko, A. Lindt, B. Rumpe, S. Storms, S. Wein, M. von Wenckstern, A. Wortmann:
    Multi-Level Modeling Framework for Machine as a Service Applications Based on Product Process Resource Models.
    In: Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control (ISCSIC’18), ACM, Sep. 2018.
  26. [KKR+18]
    S. Kriebel, E. Kusmenko, B. Rumpe, M. von Wenckstern:
    Finding Inconsistencies in Design Models and Requirements by Applying the SMARDT Process.
    In: Tagungsband des Dagstuhl-Workshop MBEES: Modellbasierte Entwicklung eingebetteter Systeme XIV (MBEES’18), Univ. Hamburg, Apr. 2018.
  27. [KSRW18]
    E. Kusmenko, I. Shumeiko, B. Rumpe, M. von Wenckstern:
    Fast Simulation Preorder Algorithm.
    In: Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development (MODELSWARD’18), pp. 256 - 267, SciTePress, Jan. 2018.
  28. [HKK+18]
    S. Hillemacher, S. Kriebel, E. Kusmenko, M. Lorang, B. Rumpe, A. Sema, G. Strobl, M. von Wenckstern:
    Model-Based Development of Self-Adaptive Autonomous Vehicles using the SMARDT Methodology.
    In: Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development (MODELSWARD’18), pp. 163 - 178, SciTePress, Jan. 2018.
  29. [FIK+18]
    C. Frohn, P. Ilov, S. Kriebel, E. Kusmenko, B. Rumpe, A. Ryndin:
    Distributed Simulation of Cooperatively Interacting Vehicles.
    In: International Conference on Intelligent Transportation Systems (ITSC’18), pp. 596-601, IEEE, 2018.
  30. [GKR+17]
    F. Grazioli, E. Kusmenko, A. Roth, B. Rumpe, M. von Wenckstern:
    Simulation Framework for Executing Component and Connector Models of Self-Driving Vehicles.
    In: Proceedings of MODELS 2017. Workshop EXE, CEUR 2019, Sep. 2017.
  31. [KRRW17]
    E. Kusmenko, A. Roth, B. Rumpe, M. von Wenckstern:
    Modeling Architectures of Cyber-Physical Systems.
    In: European Conference on Modelling Foundations and Applications (ECMFA’17), pp. 34-50, LNCS 10376, Springer, Jul. 2017.
  32. [DDE+17]
    J. Dankert, C. Dernehl, L. Eckstein, S. Kowalewski, E. Kusmenko, B. Rumpe:
    RapidCoop - Robuste Architektur durch geeignete Paradigmen für Kooperativ Interagierende Automobile.
    In: Automatisiertes und Vernetztes Fahren (AAET’17), Feb. 2017.

Supervised Bachelor/Master Theses:

  • Fuß, Christian: Modellbasierte Image Caption Generation unter Anwendung von Attention-Mechanismen in EmbeddedMontiArcDL (MA), 2019
  • Gatto, Nicola: Modeling and Training of Deep Reinforcement Learning based Architectures for Cyber-Physical Systems (MA), 2019
  • Haala, Henk: Consistency Checks of Component and Connector Reconfigurations based on EmbeddedMontiArc Dynamics (BA), 2019
  • Harputlu, Eyüp: Modellierung und Generierung des Trainings von tiefen neuronalen Netzwerkarchitekturen (BA), 2019
  • Trotta, Danilo: Visualisierungs-Aspekte einer autonomen Fahrzeug-Simulation (BA), 2019
  • Cam, Ebru: 3D-Visualisierung von Fahrzeugsimulation auf Basis von OpenStreetMap (BA), 2019
  • Porta, Pascal Maurice: A Modeling Framework for the Continuous Simulation of Self-Driving Vehicles (BA), 2019
  • Kaminski, Nils: Dynamische Verhaltensaktivierung in Komponenten- und Konnektor-Modellen (MA), 2019
  • An Application Layer Protocol for Cooperative Driving (MA), 2019
  • Zhang, Hengwen: A Distributed Simulator Architecture based on MontiSim (BA), 2019
  • Bauer, Markus: Modeling Controllers for Cooperatively Interacting Vehicles (BA), 2019
  • Meurice, Jean: Simulation of Hardware and Controller Execution in Au-tomotive Systems (BA), 2019
  • Yeverino Rodriguez, Carlos Alfredo: Multi-Target Code Generation and Training of Deep Learning Networks for Autonomous Driving (MA), 2019
  • Mönckemeyer, Felix: Model-based development of a distributed trajectory planning system for autonomous vehicles (BA), 2018
  • Rexhepi, Bardh: Time Series Analysis and Prediction for Automotive Diagnosis (MA), 2018
  • Jansen, Marvin: A Product Line for Simulator-Coupling in MontiSim (BA), 2018
  • von Oy, Christoph: Modeling and Co-Simulation of Physical Vehicle Behavior (BA), 2018
  • Pavlitskaya, Svetlana: Integration of Deep Learning Components into Autonomous Driving Architectures (MA), 2018
  • Richter, Christoph: Model-predictive Trajectory Control and Simulation for Self-driving Vehicles (MA), 2018
  • Timmermanns, Thomas: Modelling Languages for Deep Learning based Cyber-Physical Systems (MA), 2018
  • Hellwig, Alexander: Modeling and Simulation of Cooperative Vehicles with MontiCAR and the ROS Simulation Framework (BA), 2018
  • Napiorkowski, Sebastian: Leveraging Process Mining for Error Sequence Detection (MA), 2018
  • Schmidt, Deniz: Model driven development of configurable vehicle simulations (BA), 2018
  • Ilov, Petyo: Software architecture of distributed multi-user simulation of autonomously driving vehicles (MA), 2018
  • Ryndin, Alexander: Modelling of Component-and-Connector Architectures for Autonomous Vehicles (MA), 2018
  • Dao, Hoai: Similarity Measures for Categorical and Mixed Data (MA), 2018
  • Schultz, Martin: Modelling dynamic Architectures for cooperative Vehicles (MA), 2018
  • Martini, Melanie: Comparison of visualization techniques for high dimensional data (BA), 2018
  • Dalgic, Baran: C&C Modellierung autonomen Fahrverhaltens und Simulation in Gazebo (BA), 2017
  • Atouani, Abdallah: ROS basierte Simulation von C2C Kommunikation (BA), 2017
  • Görick, Philipp: Matrix Properties in Type Systems of Component and Connector Based Languages (BA), 2017
  • Schnackenberg, Alexander: Event-based Dynamic Reconfiguration of Component and Connector Architectures (BA), 2017
  • Tabone, Luca: Architectures for Data Science Automation (MA), 2017
  • Lorang, Mike: Model-based design and simulation of autonomous vehicle controllers (BA), 2017
  • Scheidt, Leon: Qualitätsmaße für Clustering-Performance-Evaluation (BA), 2017
  • Frohn, Christian: Simulation and Modeling of Vehicle-to-Vehicle Communication for Autonomously Driving Vehicles (MA), 2017
  • Sema, Albi: Software Architectures for Deep Learning based Autonomous Driving (MA), 2017