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Projects
- Generation of Naturalistic Traffic Rule Violations Using Imitation Learning
Master's Thesis at TU Munich — Using Generative Adversarial Imitation Learning to generate naturalistic traffic rule violations for autonomous driving testing.
- Assessing the Similarity of Traffic Scenarios Using Graph Neural Networks
Research project at TU Munich — Using Deep Divergence Graph Kernels (DDGK) to learn graph-based similarity embeddings for traffic scenarios, enabling clustering and classification.
- Motion Planning with Temporal Logic Specifications
Seminar work at TU Munich — Reviewing integrated task and motion planning approaches that use temporal logics (LTL, CTL, STL) to handle complex specifications such as coverage, sequencing, and temporal ordering for robotic systems.
- Motion Planning Feasibility Checker
Developed the feasibility checker module of the CommonRoad Drivability Checker Toolbox for simulation and feasibility evaluation of automated vehicle trajectories.
- Clustering Similar Traffic Scenarios
Research project at TU Munich — Using a modified unsupervised Random Forest algorithm to automatically find categories of traffic scenarios for autonomous vehicle validation.
- Emotion Analysis of Meetings Using Facial Expression Recognition
Bachelor's Thesis at Ege University — Developed a real-time facial expression recognition system to provide speakers with instant feedback on listener interest levels.