PhD topics
Our department provides the opportunity for the best graduate students to study and research in the faculty’s PhD programme.
Our topics include vehicle control and dynamics; mechatronics; self-driving cars and machine learning; road traffic modelling and control.
For the application process see the website of the faculty!
Current topics
Title: Validity of co-simulation
In the context of road traffic systems, digital twins can be developed across multiple dimensions, e.g., traffic flow, vehicular communications, intelligent transportation systems (ITS) infrastructure, and environmental factors. Creating a comprehensive digital twin that integrates all these dimensions requires the use of co-simulation, where multiple specialized simulators operate together. The main objective of this research is to seamlessly integrate heterogeneous simulation environments and to analyze the resulting synchronization mechanisms and trade-offs in accuracy and validity with scientific rigor. The used methodologies include numerical analysis and learning-based approaches. Contact: Balázs Varga, PhD
Title: Research and development of methods for driver assistance systems
The research aims at finding solutions based on information from different environmental sensors, such as LIDAR/Radar and vision based systems. The goal is to develop sensor and sensor fusion algorithms that help design intelligent vehicles. Contact: Tamás Bécsi, PhD
Title: Research on cooperative behavior of autonomous vehicles
The doctoral program aims at researching advanced driver support functions for various intelligent vehicle systems. The goal is to find new solutions by using V2X communication, while considering the expectations of safe road transport and the feasibility of control of vehicle groups. The research is aimed at controlling vehicles that communicate with their environment, using the new information that has been acquired to develop intelligent transportation systems. Contact: Tamás Bécsi, PhD
Title: Designing autonomous road vehicle decision models using artificial intelligence
The doctoral program aims at the research of the future directions of autonomous vehicles. Based on the information available of the different sensors of vehicles, the goal is to develop machine learning algorithms, especially on the area of reinforcement learning that can robustly perform the required task. Contact: Szilárd Aradi, PhD
Title: Developing model-based trajectory planning and state-estimation methods for autonomous road vehicles
The goal of the research is twofold. The first is the evaluation of the current motion planning techniques, and the development of feasible and customizable trajectory planners for self driving cars, and the second is to provide fast and reliable estimation on the feasibility of maneuvers considering vehicle dynamics. Contact: Tamás Bécsi, PhD
Title: City redesign with AI backcasting for green and sustainable multi-modal transport infrastructure
This research aims to develop an Artificial Intelligence and/or multi-objective optimization based backcasting framework capable of generating holistic city redesign strategies by starting from predefined sustainability targets, such as low rate of emission, accidents, or traffic congestion. The core of the investigation involves utilizing an integrated predictive model that simultaneously addresses multi-modal transportation development and the influence on travel demand patterns (travel behaviour change) while accurately performing emission modeling for both vehicular traffic and heating sources. Contact: Tamás Tettamanti, PhD