Thesis Student (mfd) 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 masters 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)

Qualifications :

• You are interested in autonomous driving machine learning or robotics.
• Basic knowledge of deep learning and sequence modeling.
• Hands-on experience with reading and adapting research codebases.
• Strong Python skills.
• Experience with experiments and data analysis is a plus.

Additional Information :

Employee benefits: Urban Sports Club etc.
Attractive work environment: Modern offices on-site cafeteria free parking
International company: Career opportunities in a global setting
Open corporate culture: Diverse multicultural team
Events: Regular company and team events

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No

Employment Type :

Full-time