Thesis Student (m/f/d) - AI for Automotive MCET

Trajectory prediction models aim to forecast how agents such as pedestrians or vehicles will move in the future. Many models have been proposed, but they are often evaluated under different settings. This makes it difficult to understand which approaches work best and why.

This master’s thesis focuses on systematically benchmarking major trajectory prediction models in a consistent and structured way, following their development over time. The goal is to build a clear understanding of how different modeling choices affect performance, robustness, and generalization.

Based on this analysis, the project will explore simple and effective improvements or a unified modeling approach that combines the strengths of existing methods.

Key Research Areas and Tasks

• Benchmarking major trajectory prediction models under a unified and reproducible setup
• Analysis of failure cases in trajectory prediction (e.g., collisions, unrealistic trajectories, lack of diversity)
• Development of a clean benchmarking pipeline for fair comparison
• Investigation of modeling improvements or a unified architecture
• Evaluation using common metrics (e.g., ADE, FDE)