Challenges of Artificial Intelligence and Machine Learning Software in Autonomous Vessels. Ashraf, A., Lilius, J., Porres Paltor, I., Walden, M. & Petre, L., 2021, Proceedings of International Seminar on Safety and Security of Autonomous Vessels (ISSAV’19). p.

Prediction of on-board energy usage combining physics-based modeling and machine learning. Björkqvist, J., Manngård, M., Gustafsson, W., Böling, J. & Hammarström, J., May 2021, 3rd International Conference on Modelling and Optimisation of Ship Energy Systems. Espoo.

ABOships – An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations. Iancu, B., Soloviev, V., Zelioli, L. & Lilius, J., 5 Feb 2021, In: Remote Sensing. 13, 5, p. 1-17 17 p., 988.

A Systematic Mapping Study on Edge Computing Approaches for Maritime Applications. Morariu, A-R., Ashraf, A. & Björkqvist, J., 1 Sep 2021, Euromicro DSD/SEAA 2021. IEEE Computer Society Conference Publishing Services (CPS), 8 p.

Formal Verification of COLREG-Based Navigation of Maritime Autonomous Systems. Shokri-Manninen, F., Vain, J., Walden, M., de Boer, F. (Ed.), & Cerone, A. (Ed.) (2020).  In Proceedings of SEFM 2020 -The 18th International Conference on Software Engineering and Formal Methods (pp. –) 

A survey of machine learning approaches for surface maritime navigation. Azimi, S., Salokannel, J., Lafond, S., Lilius, J., Salokorpi, M. & Porres, I., 2020, Maritime Transport VIII: proceedings of the 8th International Conference on Maritime Transport: Technology, Innovation and Research: Maritime Transport ’20.Iniciativa Digital Politècnica, p. 103 117 p.

On the Verification and Validation of AI Navigation Algorithms.Porres, I., Azimi, S., Lafond, S., Lilius, J., Salokannel, J. & Salokorpi, M., 2020, Global OCEANS 2020.

Scenario-based Testing of a Ship Collision Avoidance System. Porres, I., Azimi, S. & Lilius, J., 2020, 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, p. 545-552.

Edge-based Vibration Monitoring of Marine Vessel Engines. Morariu, A-R., Lund, W., Lundell, A., Björkqvist, J. & Anders, Ö., 14 Oct 2020, 12th Symposium on High-Performance Marine Vehicles : HIPER’20. Volker, B. (ed.). Technische Universität Hamburg-Harburg, p. 239-250 12 p.

Using Digital Twin Technology to Ensure Data Quality in Transport Systems. Björkqvist, J., Manngård, M. & Lund, W., 2020, Proceedings of TRA2020, the 8th Transport Research Arena: Rethinking transport – towards clean and inclusive mobility. Helsinki, (Traficom Research Reports; no. 7).

Comparing CNN-Based Object Detectors on Two Novel Maritime Datasets. Soloviev, V., Farahnakian, F., Zelioli, L., Iancu, B., Lilius, J. & Heikkonen, J., 2020, 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE

Estimation of propeller torque in azimuth thrusters. Manngård, M., Lund, W., Keski-Rahkonen, J., Nänimäinen, J., Saarela, V-P., Björkqvist, J. & Toivonen, H., 2019, In: IFAC-PapersOnLine. 52, 21, p. 140–145

Virtual sensing in marine systems. Manngård, M., Lund, W., Björkqvist, J., Toivonen, H., Sin, G. (ed.), Bagterp Jørgensen, J. (ed.) & Kjøbsted Huusom, J. (ed.), 2019.

Building and operating a UHF-band test network for providing mission critical marine communication in the Turku archipelago. Björkqvist, J., Lund, W., Soloviev, V., Tuulos, K. & Suominen, K., 2018, Åbo Akademi University. 17 p.

IoT at Sea. Nybom, K., Lund, W., Lafond, S., Lilius, J., Björkqvist, J., Suominen, K. & Tuulos, K., 2018, 2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, p. 1–7 7 p.


Application of a Distributed Software System on an Autonomous Maritime Vehicle, Brushane, F. 2021. 

Development of autonomous navigation systems for maritime applications, Storbacka, M. 2021. 

Reglersystem förkurs och hastighet för autonomtfartyg, Fröjdö, L. 2021. (Bachelor’s thesis)

Learning Maritime Surface Ship Navigation by Imitation Learning, Landais, C. (INSA Double degree), 202. 

Identifying Risk-Prone Behaviour of Sea Farers by Using Explainable AI, Fouqué, N. (INSA Double degree), 2021. 

Learning Maritime Surface Ship Navigation by Reinforcement Learning: with a focus on designing the framework and collecting the input set, Nyberg, R.  (ongoing)

Integration of ML models in the 3D simulation environment AISimLive, Aarnio J. (ongoing)

Learning autonomous maritime navigation with offline reinforcement learning and marine traffic data. Westerlund, J. 2021. 

A simulator for evaluating machine-learning algorithms for autonomous ships, Hupponen, K. 2020. 

COLREG compliant collision avoidance using reinforcement learning, Penttinen, S. 2020. 

Fuzzy logic and unmanned surface vehicles : Implementing collision avoidance in Python, Aura, E. 2018. 

Object detection using LIDAR in maritime scenarios. Wessman, M. 2018. 


Rapport de stage technicien long: System Developer (Développeur système), Vettoretti. E. 2021. ESIGELEC (ERASMUS Internship report)

Stage technicien long: Åboat, boat project. Branchu, M. 2021. ESIGELEC. (ERASMUS Internship report)