Sevenster, A., Farah, H., Abbink, D.A. and Zgonnikov, A. (2023). Response times in drivers' gap acceptance decisions during overtaking. Transportation research part F: traffic psychology and behaviour. [https://www.sciencedirect.com/science/article/pii/S1369847823000517]
Schumann, J.F., Kober, J. and Zgonnikov, A. (2023). Benchmarking behavior prediction models in gap acceptance scenarios. IEEE Transactions on Intelligent Vehicles. [https://ieeexplore.ieee.org/abstract/document/10043012/]
Koerten, K, Abbink, D.A. and Zgonnikov, A. (2023). Haptic Shared Control for Dissipating Phantom Traffic Jams. IEEE Transactions on Human-Machine Systems. [https://arxiv.org/abs/2212.11591]
Siebert, L.C., Lupetti, M.L., Aizenberg, E., Beckers, N., Zgonnikov, A., Veluwenkamp, H., Abbink, D.A., Giaccardi, E., Houben, G.-J., Jonker, C. M., Van den Hoven, J., Forster, D. and Lagendijk, R.L. (2022). Meaningful human control: actionable properties for AI system development. AI and Ethics. [https://link.springer.com/article/10.1007/s43681-022-00167-3]
Tognan, A., Laurenti, L. and Salvati, E. (2022). Contour Method with Uncertainty Quantification: A Robust And Optimised Framework via Gaussian Process Regression. Experimental Mechanics. [https://link.springer.com/article/10.1007/s11340-022-00842-w]
Siebinga, O., Zgonnikov, A. and Abbink, D. (2022). A human factors approach to validating driver models for interaction-aware automated vehicles. ACM Transactions on Human-Robot Interaction (THRI). [https://dl.acm.org/doi/abs/10.1145/3538705]
Baymler Mathiesen, F., C Calvert, S.C. and Laurenti, L. (2022). Safety certification for stochastic systems via neural barrier functions. IEEE Control Systems Letters. [https://arxiv.org/abs/2206.01463]
Salvati, E., Tognan, A., Laurenti, L., Pelegatti, M. and De Bona, F. (2022). A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing. Materials & Design. [https://www.sciencedirect.com/science/article/pii/S0264127522007110]
Adams, S., Lahijanian, M. and Laurenti, L. (2022). Formal control synthesis for stochastic neural network dynamic models. IEEE Control Systems Letters. [https://arxiv.org/abs/2203.05903v2]
Cardelli, L., Kwiatkowska, M. and Laurenti, L. (2021). A Language for Modeling and Optimizing Experimental Biological Protocols. Computation. [https://www.mdpi.com/2079-3197/9/10/107]
Sevenster, A., Farah, H., Abbink, D.A. and Zgonnikov, A. (2023). Response times in drivers' gap acceptance decisions during overtaking. Transportation research part F: traffic psychology and behaviour. [https://www.sciencedirect.com/science/article/pii/S1369847823000517]
Schumann, J.F., Kober, J. and Zgonnikov, A. (2023). Benchmarking behavior prediction models in gap acceptance scenarios. IEEE Transactions on Intelligent Vehicles. [https://ieeexplore.ieee.org/abstract/document/10043012/]
Koerten, K, Abbink, D.A. and Zgonnikov, A. (2023). Haptic Shared Control for Dissipating Phantom Traffic Jams. IEEE Transactions on Human-Machine Systems. [https://arxiv.org/abs/2212.11591]
Siebert, L.C., Lupetti, M.L., Aizenberg, E., Beckers, N., Zgonnikov, A., Veluwenkamp, H., Abbink, D.A., Giaccardi, E., Houben, G.-J., Jonker, C. M., Van den Hoven, J., Forster, D. and Lagendijk, R.L. (2022). Meaningful human control: actionable properties for AI system development. AI and Ethics. [https://link.springer.com/article/10.1007/s43681-022-00167-3]
Tognan, A., Laurenti, L. and Salvati, E. (2022). Contour Method with Uncertainty Quantification: A Robust And Optimised Framework via Gaussian Process Regression. Experimental Mechanics. [https://link.springer.com/article/10.1007/s11340-022-00842-w]
Siebinga, O., Zgonnikov, A. and Abbink, D. (2022). A human factors approach to validating driver models for interaction-aware automated vehicles. ACM Transactions on Human-Robot Interaction (THRI). [https://dl.acm.org/doi/abs/10.1145/3538705]
Baymler Mathiesen, F., C Calvert, S.C. and Laurenti, L. (2022). Safety certification for stochastic systems via neural barrier functions. IEEE Control Systems Letters. [https://arxiv.org/abs/2206.01463]
Salvati, E., Tognan, A., Laurenti, L., Pelegatti, M. and De Bona, F. (2022). A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing. Materials & Design. [https://www.sciencedirect.com/science/article/pii/S0264127522007110]
Adams, S., Lahijanian, M. and Laurenti, L. (2022). Formal control synthesis for stochastic neural network dynamic models. IEEE Control Systems Letters. [https://arxiv.org/abs/2203.05903v2]
Cardelli, L., Kwiatkowska, M. and Laurenti, L. (2021). A Language for Modeling and Optimizing Experimental Biological Protocols. Computation. [https://www.mdpi.com/2079-3197/9/10/107]
Others
Siebinga, O., Zgonnikov, A. and Abbink, D.A. (2023). Merging in a Coupled Driving Simulator: How do drivers resolve conflicts?. under review. [https://arxiv.org/abs/2308.04842]
Ramakrishnan Srinivasan, A., Schumann, J., Wang, Y., Lin, Y.-S., Daly, M., Solernou, A., Zgonnikov, A., Leonetti, M., Billington, J. and Markkula, G. (2023). The COMMOTIONS Urban Interactions Driving Simulator Study Dataset. under review. [https://arxiv.org/abs/2305.11909]
Zgonnikov, A., Beckers, N., George, A., Abbink, D.A. and Jonker, C. (2023). Subtle motion cues by automated vehicles can nudge human drivers’ decisions: Empirical evidence and computational cognitive model. under review. [https://psyarxiv.com/3cu8b]
Fundamental AI
Jackson, J., Laurenti, L., Frew, E., & Lahijanian, M. (2021). Synergistic Offline-Online Control Synthesis via Local Gaussian Process Regression. IEEE Conference on Decision and Control (CDC)
Jackson, J., Laurenti, L., Frew, E., & Lahijanian, M. (2021, May). Strategy synthesis for partially-known switched stochastic systems. International Conference on Hybrid Systems: Computation and Control (pp. 1-11).
Delimpaltadakis, G., Laurenti, L., & Mazo Jr, M. (2021). Abstracting the sampling behaviour of stochastic linear periodic event-triggered control systems. IEEE Conference on Decision and Control (CDC)
Wicker, M., Laurenti, L., Patane, A., Paoletti, N., Abate, A., & Kwiatkowska, M. (2021). Certification of iterative predictions in Bayesian neural networks. In Uncertainty in Artificial Intelligence (pp. 1713-1723). PMLR.
Siebert, L. C., Lupetti, M. L., Aizenberg, E., Beckers, N., Zgonnikov, A., Veluwenkamp, H., Abbink, D., Giaccardi, E., Houben, G.-J., Jonker, C. M., Hoven, J. van den, Forster, D., & Lagendijk, R. L. (2021). Meaningful human control over AI systems: Beyond talking the talk. http://arxiv.org/abs/2112.01298
Peschl, M., Zgonnikov, A., Oliehoek, F. A., & Siebert, L. C. (2021). MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active Learning. http://arxiv.org/abs/2201.00012
Applied AI
Alessandro Tognan, Luca Laurenti, & Enrico Salvati. (2022). Contour Method with Uncertainty Quantification: A Robust And Optimised Framework via Gaussian Process Regression. Experimental Mechanics
Cardelli, L., Kwiatkowska, M., & Laurenti, L. (2021). A Language for Modeling and Optimizing Experimental Biological Protocols. Computation, 9(10), 107.
Siebinga, O., Zgonnikov, A., & Abbink, D. (2021). Validating human driver models for interaction-aware automated vehicle controllers: A human factors approach. http://arxiv.org/abs/2109.13077
Zgonnikov, A., Abbink, D., & Markkula, G. (2020). Should I stay or should I go? Evidence accumulation drives decision making in human drivers. https://doi.org/10.31234/osf.io/p8dxn
