In the fight against climate change and coping with the pressures arising from urbanisation, cities worldwide make solutions modifying pollutant emissions and thermal comfort. At present, road transport contributes a significant amount to the total carbon dioxide emissions in the EU. Thus, cities look for practical strategies to make their transport system more efficient and sustainable, and SDA Group is developing novel reinforcement learning methods to aid in this goal while maximising the air quality and minimising the carbon dioxide emissions.
Availability of a global navigation and timing solution provided by GNSS has accelerated the appearance of various applications supporting sustainable development in the forms of improved safety, decreased environmental burden, new business opportunities and increased quality of life. However, the applications have demands on the PVT reliability, availability and accuracy that GNSS was not developed to support. The need for an ubiquitous GNSS that can adapt to the signal environment is self-evident. The development of accurate deep learning (DL) methods has provided huge improvements in computation in many disciplines. However, GNSS and navigation field has been slow to introduce DL into research. SDA group provides novel machine learning methods for improving the performance of a GNSS receiver and providing ubiquitous adaptive GNSS.
Computer vision provides crucial information for various applications requiring reliable navigation and situational awareness. SDA group develops novel methods for Simultaneous Localization and Mapping (SLAM) in challenging environments and 3D object tracking for improved safety for autonomous systems.