Nicolas Lampe

Nicolas Lampe is a researcher at Osnabrück University of Applied Sciences, affiliated with the Faculty of Engineering and Computer Science and the Department of Technical Informatics. His work focuses on hybrid approaches combining model-based and data-driven methods for tire-road friction estimation, with applications in autonomous driving and vehicle safety systems. He earned his B.Sc. and M.Sc. in Mechanical Engineering from Leibniz University Hannover.

Nicolas Lampe's research emphasizes vehicle dynamics, sensor data analysis, and machine learning for transportation systems. Key projects include:

• Hybrid friction estimation combining physical models and neural networks
• Recurrent neural network applications for time-series friction prediction
• Experimental validation of estimation algorithms using onboard sensors

His publications highlight trends in automotive mechatronics, with recent works exploring:

• Transformer architectures for sensor data processing
• Active excitation techniques for improved estimation
• Impact of gradient/cross-slope on friction modeling

Lampe has delivered lectures on topics like:

• Fahrerassistenzsysteme (Driver Assistance Systems)
• Neural network applications in vehicle technology