- During your Master thesis, you will review the existing literature on predictive safety and stability filters, as well as related concepts from Model Predictive Control.
- You will design and implement a new advanced driver assistance and autonomous driving safety function using a recently proposed stability filter methodology.
- Furthermore, you will verify safety and stability in simulation using an advanced vehicle simulation environment, considering challenging driving scenarios with desired inputs generated by humans or imitative learning algorithms.
- In addition, you will refine the methods to improve practicality and include possible additional targets depending on the driving situation and environment.
- Last but not least, you will support the implementation and testing on a real vehicle.
- Education: Master studies in the field of Electrical Engineering, Mechanical Engineering or comparable with a focus on Control Engineering
- Experience and Knowledge: in control theory, especially in Model Predictive Control (MPC); good programming skills, especially in Python; practical experience beneficial
- Personality and Working Practice: independent, enthusiastic and creative
- Languages: good in German or English
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Master Thesis Model Predictive Stability Filters for Advanced Driver Assistance Systems - Renningen, Deutschland - Robert Bosch GmbH
Beschreibung
Master Thesis Model Predictive Stability Filters for Advanced Driver Assistance Systems
Robert-Bosch-Campus 1, 71272 Renningen, Germany
Full-time
Robert Bosch GmbH
Company Description
At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.
The Robert Bosch GmbH is looking forward to your application
Job Description
Advances in learning-based control and the increasing demand for human-machine interaction are driving the need for modular safety certifications in modern control systems. Prominent areas include surgical robotics, automated driving and smart factories. Recent research efforts are addressing this challenge through so-called predictive safety filter methods, which allow the safe integration of, for example, human-in-the-loop inputs or reinforcement learning algorithms into safety-critical systems. While these safety filters ensure persistent constraint satisfaction, additional safety and stability properties are often desired, which may vary depending on the specific use case or environmental context. To this end, recent developments aim at extending predictive safety filters with additional desirable closed-loop properties such as bounded convergence and asymptotic stability.
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.
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Daniel Schwarzer (Functional Department)
Elias Milios (Functional Department)
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