Moritz Werling

Karlsruhe University of Education, BMW Group (Germany)

Papers

Total Citations

121

H-Index

About

Moritz Werling is a leading researcher at the intersection of robotics, autonomous driving, and control systems, with a focus on enabling safe, human-like navigation for car-like robots. His seminal 2008 work on navigating car-like robots in unstructured environments introduced an obstacle-sensitive cost function for path planning, using an informed graph search to derive feed-forward control terms—a foundational approach that has garnered 95 citations. More recently, Werling has advanced the field of Inverse Reinforcement Learning (IRL) by incorporating safety constraints into sampling-based methods, allowing robotic systems to learn cost functions from human demonstrations while ensuring operational safety (2021, 14 citations). His contributions extend to practical implementations in automated driving, as detailed in his German-language book "Optimale aktive Fahreingriffe" (2017), which explores the optimization of active driving assistants for safety and comfort across automation levels. Werling’s work bridges theoretical optimization and real-world robotics, making him a key figure in developing robust, human-aware autonomous systems.