Master Thesis Data-Driven Modeling of Inverse Lateral Motorcycle Dynamics

In the central research division of Robert Bosch GmbH in Renningen you will be part of a team that is working on the motorcycle safety systems of tomorrow. Our shared goal is to reduce the risk of accidents for motorcyclists while maintaining high riding comfort and enjoyment. A key challenge is the precise modeling and estimation of inverse lateral motorcycle dynamics. This makes it possible to determine the necessary control inputs for a desired vehicle state. A new approach is to learn this dynamic using machine learning or deep learning.

• As a part of your Master thesis you will evaluate classical and deep learning-based methods for time-series prediction.
• To this end you will analyze motorcycle dynamics data.
• In addition you will identify suitable modeling approaches implement them in PyTorch and assess the results based on real datasets from test rides.
• We offer you the opportunity to collaborate in an interdisciplinary team with experts in deep learning and rider assistance systems. You will gain access to powerful GPU resources and extensive vehicle dynamics data as well as engage in practically relevant research with direct application in safety-critical systems.

Qualifications :

Additional Information :

Start :  according to prior agreement

Duration :  6 months

Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

Need further information about the job

Alexander Lutzke (Functional Department)

49

#LI-DNI

Remote Work : No

Employment Type : Full-time

Key Skills

Dermatology,Communication,Excel,Furniture,Airlines,Jboss

Experience : years

Vacancy : 1