Témakiírások
Magyarázható mesterségesintelligencia-megoldások a C-ITS döntéstámogatására
témakiírás címe
Magyarázható mesterségesintelligencia-megoldások a C-ITS döntéstámogatására
intézmény
doktori iskola
témakiíró
tudományág
témakiírás leírása
The automotive industry, road traffic authorities, and road operators are experiencing a significant shift toward smarter, more connected transportation ecosystems, driven by the proliferation of cooperative intelligent transport systems (C-ITS), the complex solutions of increasingly collective behaviours in advanced driver assistance systems (ADAS), and autonomous vehicles (AVs). The remarkable development of ICT has facilitated traffic signal strategies to control conflicting traffic flow more efficiently at intersections and signalised traffic networks. It has allowed the pursuit of detailed spatiotemporal information of all moving objects, including vehicles, non-motorised modes, and public transits, on a network in real-time.
The research topic focuses on solving challenges related to the application of AI methods in C-ITS systems due to the black-box nature of modern AI methods.
The candidate should enhance and develop explainable AI (XAI) methods that make the model decision more transparent and effectively determines the necessary evidence for the model prediction. The topic is interdisciplinary, combining AI, machine learning, transportation aspects, ideal for motivated candidates with strong programming and mathematical backgrounds.
References
• ETSI TR 102 638 V2.1.1 (2024-04), Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Release 2
• Liu, X., et al. (2025). Explainable AI in Multi-Agent Systems: Advancing Transparency with Layered Prompting. ResearchGate.
• Smith, J., & Johnson, A. (2025). Generative AI for decision-making: A multidisciplinary perspective. Journal of Innovation in Digital Ecosystems.
• Calvaresi, D., et al. (Eds.). (2025). Explainable, Trustworthy, and Responsible AI and Multi-Agent Systems. Springer.
• Kim, H., et al. (2025). Multi-task reinforcement learning and explainable AI-Driven platform for clinical decision support. Scientific Reports.
• Wang, L., et al. (2025). Designing with Multi-Agent Generative AI: Insights from Industry Practitioners. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
The research topic focuses on solving challenges related to the application of AI methods in C-ITS systems due to the black-box nature of modern AI methods.
The candidate should enhance and develop explainable AI (XAI) methods that make the model decision more transparent and effectively determines the necessary evidence for the model prediction. The topic is interdisciplinary, combining AI, machine learning, transportation aspects, ideal for motivated candidates with strong programming and mathematical backgrounds.
References
• ETSI TR 102 638 V2.1.1 (2024-04), Intelligent Transport Systems (ITS); Vehicular Communications; Basic Set of Applications; Release 2
• Liu, X., et al. (2025). Explainable AI in Multi-Agent Systems: Advancing Transparency with Layered Prompting. ResearchGate.
• Smith, J., & Johnson, A. (2025). Generative AI for decision-making: A multidisciplinary perspective. Journal of Innovation in Digital Ecosystems.
• Calvaresi, D., et al. (Eds.). (2025). Explainable, Trustworthy, and Responsible AI and Multi-Agent Systems. Springer.
• Kim, H., et al. (2025). Multi-task reinforcement learning and explainable AI-Driven platform for clinical decision support. Scientific Reports.
• Wang, L., et al. (2025). Designing with Multi-Agent Generative AI: Insights from Industry Practitioners. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
helyszín
ITDI
jelentkezési határidő
2026-01-15