Description

In this Master thesis role, you will dive into the world of automated driving, focusing on multi-sensor fusion and machine learning. Your work will involve conducting literature reviews, developing models, and collaborating with experts to enhance real-world deployment strategies.

Requirements

• •Studies in computer science, electrical engineering, robotics, or related field with focus on machine learning, deep learning, or artificial intelligence
• •Strong understanding of Transformer-based models and attention mechanisms
• •Experience with sensor fusion concepts
• •Proficient programming skills in Python, PyTorch, and C++
• •Ability to support independently and as part of a team
• •Willingness to collaborate with university supervisors and adapt to project requirements
• •Good English skills; German is a plus

Tasks

• •Conduct an in-depth literature review on Transformer architectures
• •Research multi-modal fusion techniques for automated driving
• •Evaluate robust perception methods for real-world applications
• •Support experimental evaluation of high-level fusion approaches
• •Utilize public and BMW multimodal datasets for testing
• •Select and develop Transformer-based fusion models for multi-sensor inputs
• •Explore efficiency strategies for low-latency deployment on in-vehicle hardware
• •Assist in evaluating accuracy and robustness in simulated driving scenarios
• •Collaborate with software developers and ML engineers at the Autonomous Driving Campus

Tools & Technologies

Benefits