PhD - End-2-End Trainable System for Autonomous Driving with Introspection

Current automated driving systems are structured in a hybrid manner, meaning that classical model-driven approaches are combined with data-driven AI approaches. So far all modules are developed or trained independently based on module-related key performance indicators. As a second step system-related evaluation is conducted in software-in-the-loop tests and closed-loop in the vehicle. The main drawbacks are on the one hand, that the independent optimization of modules offers no guarantee of a global system-related optimum and on the other hand, the manual experience-based analysis of the resulting system to determine the performance-related weak points in the architecture. The slow system design process is also a disadvantage.

• The goal of this PhD thesis is to research the system aspects of a ground breaking innovation in system design: End-2-End trainable systems.
• You will edit following research questions: How to efficiently train a system that is composed of AI sub-modules (efficient loss propagation)? How to train a hybrid system? And you will also research safety-related measures that allow introspection of the system.
• In your PhD thesis you will develop novel machine learning approaches with a focus on Video, Radar and LiDAR.
• Furthermore, you will evaluate your algorithms on public benchmark data sets and internal real-world data sets - offline as well as online.
• In addition, you will contribute to the scientific community with publications on top machine learning, system and robotics conferences as well as journals (NIPS, ICML, ICLR, CVPR, ICCV, IROS, TPAMI or IV).
• Last but not least, you will take responsibility and work in an agile as well as diverse research team with other PhD students and with a strong link to autonomous driving system research projects.

Requirements

• Education: excellent degree (Master/Diploma) in Computer Science, Robotics, Electrical Engineering, Mathematics or related field
• Experience and Knowledge: profound knowledge of machine learning algorithms and principles, preferably deep learning as well as proven programming skills in Python and C++
• Personality and Working Practice: open-minded, logical thinking, goal- als well as team-oriented
• Languages: fluent in English (written and spoken), German is a plus