Kai Wah Chan

Kai Wah Chan has a BSc and MSc in industrial mathematics from the University of Bremen. From 2017 to 2022, he was a PhD candidate at the Center for Industrial Mathematics (ZeTeM) and the German Aerospace Center (DLR) Bremen funded by the European Space Agency. His thesis was on optimization-based reachability analysis for landing scenarios. He has been working at TOPAS Industriemathematik Innovation since 2021, currently working on the MUTIG-VORAN project.

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Presentation

Optimizing safe stop trajectories: synthesizing algorithms for autonomous vehicles

In the realm of autonomous driving, ensuring a secure and seamless halt is imperative across diverse scenarios, ranging from routine stops at traffic lights to critical situations involving navigation failures or communication errors. This presentation unveils a novel methodology for swiftly calculating safe stop trajectories. The approach harmonizes machine learning tools with classical algorithms rooted in optimization and optimal control theory. Notably, it harnesses the underutilized feasibility correction method, leveraging parametric sensitivity analysis to significantly expedite computation. This research contributes a nuanced perspective to the pursuit of enhancing safety and efficiency in autonomous vehicle systems.

The audience will learn:

• Efficient computation of safe stop trajectories for autonomous vehicles based on optimal control theory
• Combining classical optimization algorithms and machine learning techniques for the clustering of static traffic situations
• Applying the feasibility correction method for accelerated computations based on parametric sensitivity analysis