Open Access

The eye is often described as the most important human sensory organ. For visually impaired people, this sense is partially or completely absent. An important goal for an inclusive society is to enable visually impaired people to participate in everyday life, such as work and sport. This is why assistance systems are being developed that use or enhance other senses. Technology makes this levelling possible and offers new potential. Climbing represents an excellent example of a manageable but nonetheless challenging hand-eye coordination problem. A visual sensor system is required to localize wall geometry and climbing holds. The most critical step is to present the data in an intuitive and comprehensive way using other senses. Taste and smell do not allow a complete representation of three-dimensional features. The sense of touch is important as soon as the hold is within reach. Spatial hearing offers great potential, but also many challenges, such as the simultaneous acoustic representation of the holds, their distance, and position. The aim of the presented system is to output a synthetically generated signal on headphones in such a way that a hold can be localized as if it were actually emitting a signal.

Autonomous Driving (AD) has advanced significantly in recent years, yet widespread deployment remains limited. One of the most enduring challenges in Autonomous Vehicle (AV) development is planning a safe, comfortable, and efficient motion in complex, real-world environments. This thesis addresses motion planning across three distinct domains: urban shuttles, passenger vehicles, and truck-trailer systems. It contributes practical insights and novel approaches toward scalable autonomous mobility. The first part of this work presents an integrated motion planning framework for the Continental Urban Mobility Experience (CUbE) driverless shuttle. Extensive real-world testing over several years highlights the system’s robustness and underscores the importance of long-term validation in urban settings. Key innovations include a multi-layered planning stack and a data-driven motion forecasting approach that enhances interaction with human traffic participants. The second part investigates the behavior of human drivers in understructured traffic environments. Those are areas that fall between well-defined road systems and fully unstructured spaces. A novel analysis framework is introduced for mining patterns from naturalistic trajectory datasets, enabling AVs to better blend into human traffic and navigate ambiguous scenarios with improved predictability and safety. The final part of the thesis explores Deep Reinforcement Learning (DRL) for planning and controlling complex truck-trailer maneuvers. A DRL-based approach is developed and evaluated in simulated environments, demonstrating the method’s potential to handle the nonlinear dynamics of articulated vehicles. These contributions advance the field of motion planning by combining theoretical insights, system-level integration, and empirical evaluation. They offer pathways for improving AV behavior across diverse platforms and use cases, ultimately supporting the broader adoption of AD technologies.

We developed a waveguide crossing in our silicon nitride integrated photonics platform based on an adiabatic coupler design. It shows low-loss, large spectral range and high fidelity for use as quantum NOT-gate.

The Intergovernmental Panel on Climate Change concludes that climate change has already caused substantial damages at the current 1.2°C of global warming and that warming of 1.5°C would elevate risks of a wide-range of climate tipping points. For example, wet-bulb temperatures are already exceeding safe levels, and the melting of the Greenland and West Antartic ice sheets would lead to over ten metres of sea level rise, representing an existential threat to coastal cities, low-lying nation states, and human wellbeing worldwide. We call for a broad scientific discussion about a stricter and more ambitious climate target of 1.0°C by the end of this century. Comprehensive electrification and highly renewable energy systems offer a pathway to sub-1.5°C futures through rapid defossilisation and large-scale, electricity-based carbon dioxide removal. Independent scenarios show that restoring a stable and safe climate is attainable with coordinated policy and economic support.