Artificial intelligence (AI) is revolutionizing the mobility of the future. Traffic is becoming more efficient, safer and more environmentally friendly, enabling mobility and logistics that are accessible to everyone, are demand-oriented, and are nevertheless cost-effective. Target-driven testing in real test environments and demonstration to the public with the participation of all citizens increases visibility for society, business, politics and science, thus creating a showcase effect. Only such a showcase project can create acceptance and trust at an early stage, realize adequate usage behavior and promote social dialogue. Furthermore, additional fields of action will be identified, since not all questions from society, politics, etc. are known today and need to be clarified at an early stage.

The innovative approach of the project is to build a software stack with AI methods for autonomous driving and logistics, which uses machine learning methods to capture (perception) and foresee (prediction) the current situation. This can be used in different scenarios and areas of application. It makes it possible to equip vehicles with their own intelligence and implement additional capabilities such as planning and monitoring. With the help of the innovative approach of distributed intelligence, a vehicle can expand its own perception into areas that it does not capture locally.

The digitization of the infrastructure for autonomous driving is one of the main goals of the project. The ultimate goal is to make the data from all infrastructure components, processes and users available on a common digital platform. The data is of crucial importance for AI and management platforms in order to enable safe autonomous driving scenarios and coordinated interactions between the actors (i.e. drivers, passengers, service providers, authorities). The intelligent solution is implemented on three levels: digitization of the infrastructure, AI-enabled intelligent processes and a global digital platform with associated tools. Each level of the software stack to be developed must take into account the heterogeneous and cross-domain technologies that support automated and connected driving and the interaction between stakeholders. Data of different levels of granularity, AI models and services are to be made available in such a way that the establishment of new business models and ecosystems is stimulated. This is intended to lay the foundation for platform economy in the field of mobility and transport.