The development of autonomous machines is changing the way we interact with them and requires new development methods and approaches. The evolution is shifting from traditional algorithms and research to simulation and AI-based methods. High-performance hardware enables sensor fusion and neural networks to work in tandem, thereby increasing autonomy and task processing capabilities. To maintain efficiency when training neural networks, a simulation-based approach is imperative. This approach offers distinct advantages, including monitoring, testing and continuous improvement using digital twins. As experts in digitalization, we support this with comprehensive services and capabilities ranging from simulators to digital lifecycle management.

• The needed steps for developing and maintaining autonomous systems. The important differences between traditional development and simulation
• How the evaluation and fusion of sensors can be designed efficiently and reliably right from the development stage
• The main benefits of digital twins and simulation-based approaches to the development of autonomous systems