Supervised Theses 🎓

Master’s Theses

• Xinyue Cui (2024). Determining the Longitudinal and Lateral Acceleration of a Bicycle Using a Smartphone Mounted on Its Handlebar (Master’s thesis, RWTH Aachen University).
• Xia Yan (2024). Learning-Based Motion Planner for Connected and Automated Vehicles (Master’s thesis, RWTH Aachen University).
• Ruizhang Zhou (2024). AI-Based Generation of Testing Scenarios for Motion Planners on Connected and Automated Vehicles (Master’s thesis, RWTH Aachen University).
• Chang Che (2025). Constraint-Enforced Multi-Agent Reinforcement Learning for Safe Motion Planning of Connected and Automated Vehicles (Master’s thesis, RWTH Aachen University).

Bachelor’s Theses

• Xilei Chen (2024). Imitation Learning from Optimization-Based Motion Planners for Connected and Automated Vehicles (Bachelor’s thesis, RWTH Aachen University).
• Lebo Liang (2024). Safe Multi-Agent Control: Integrating Control Barrier Functions with Reinforcement Learning (Bachelor’s thesis, Aachen University of Applied Sciences).
• Raphael Malig (2024). Investigating the Effect of Discretizing Continuous Action Space in Multi-Agent Reinforcement Learning for Motion Planning (Bachelor’s thesis, RWTH Aachen University).
• Andreas Pletschko (2024). Leveraging Temporal Dependencies for Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning in Motion Planning (Bachelor’s thesis, RWTH Aachen University).
• Mohamed Shamekh (2024). Model-Predictive-Control-Based Safe Multi-Agent Reinforcement Learning for Motion Planning (Bachelor’s thesis, RWTH Aachen University).
• Omar Sobhy (2024). Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning Through Learning-Based Dynamic Prioritization (Bachelor’s thesis, RWTH Aachen University).