AImotion

The scientific director AININ, who is responsible for the topic of mobility, has taken over the role of AI Node Speaker. As the central scientific contact person, he is responsible for the overall coordination as well as the strategic development of the AI mobility node in coordination with the university management. Within and outside the university, he represents the AI mobility node in committees and at (specialist) events. Within the AI node, he or she is responsible for scientific coordination and chairs central committees and meetings. As a permanent member of the appointment committees for the new AI mobility professorships, he plays a central role in the staffing of the AI mobility node.

All scientists in the AI mobility node are assigned to one of the three application clusters or the central cross-sectional cluster. A Cluster Speaker is to be appointed from among the respective clusters, analogous to existing research structures at the THI. 

Cluster Speaker

Digital Production: Prof. Dr. Alexander Schiendorfer

Autonomous Driving: Prof. Dr. Torsten Schön

Unmanned Flying: Prof. Dr. Gerhard Elsbacher

Cross-Cutting Clusterr: Prof. Dr. Munir Georges 

Strategic control functions for the AI mobility node are performed by the Steering Committee. It is the central decision and decision-making body for the AI node. Quarterly meetings are held to report on the status of the various AI projects, budgets and deadlines with regard to the overall strategy, and to make necessary decisions. The Steering Committee is composed of the THI President, the Vice President Research, the AI node spokesperson, the four cluster spokespersons and, representative for the AI endpoints from the university of an AI endpoint, a representative from the university management. The president of the THI is the chairman of this committee,

The Steering Committee will be supported by an AI Mobility Advisory Board. It should be composed of external experts in the field of AI and/or mobility from science and industry. The advisory board will focus on technological and project-oriented aspects as well as quality assurance. The AI node will also receive strategic and technical advice from the external experts, for example in the planning and implementation of use cases.

The aim of the professorship is to research the advantages of AI processes in digital production and to bring them to application. One focus is the development and use of AI methods for quality assessment in production processes, so that quality assurance steps can be reduced or eliminated. By means of online feedback of correction parameters derived from the AI models, a constant product quality with greatly reduced scrap is to be guaranteed.

The professorship is to be closely interlinked with the AUDI endowed professorship and with a planned institute in the Industry 4.0 topic area. In this competence field, 8 professorships are currently conducting research on topics relevant to AI applications, such as networking and cloud architectures in production, information models and semantics, and additive manufacturing technologies.

A further topic area that is considered in this application cluster is the interaction of people and learning systems in production processes.

Job holder: Prof. Dr. Alexander Schiendorfer

In addition to cloud-based services, edge computing (calculations and analyses at the very ends of an IT topology) is playing an increasingly important role in automated and networked driving. All computing operations of a networked mobility system that take place in a vehicle can be assigned to edge computing.

The aim of the professorship is to research new methods and procedures at the interface between hardware and software for the use of AI methods in edge computing for automated driving. The focus is on the consideration of the available computing resources.

Job holder: Prof. Dr. Richard Membarth

The aim of the professorship is to use AI-based methods to generate a comprehensive image of the vehicle environment in real time from different sensors as a basis for automated driving. A robust sensor data fusion, which considers sensor data of different origin (e.g. from the vehicle or from the infrastructure) and different quality, is the focus of the research. Deep knowledge in the field of optical sensors (lidar, kamara) and radar sensors is necessary for this professorship.

The software for autonomous mobility systems is highly complex due to the high degree of networking of the large number of sensors and control units. The goal of the professorship is to extend established software architectures for automated driving in order to efficiently integrate AI algorithms and thereby meet the fail-operational requirements for autonomous systems.

AI can also be used to build intelligent and adaptive software systems and to support more efficient software development processes. Especially in the field of testing software for autonomous mobility it is expected that AI methods can make an important contribution. The professorship will also focus its research on these aspects of software development.

Job holder: Prof. Dr. Lenz Belzner

The aim of the professorship is to work on new techniques for the management and analysis of mobility data and to gain insights into mobility ecosystems using unsupervised learning methods. The efficient access to huge amounts of data is of great importance in the development as well as in the protection of automated vehicles, especially in connection with the use of AI methods. The automated identification of relevant traffic scenarios by means of AI methods and the associated possibilities of time-lapse are very important for the further development and testing of sensors and planning algorithms. The efficient data management in mobility centres forms the basis for smart services.

Job holder: Prof. Dr. Patrick Cato 

The real-time capability and the development of AI systems for mobility applications that do not require a lot of computing power are the focus of this professorship. Many system components required for automated mobility require real-time capability, e.g. detection and tracking of objects or control systems. In order to make the advantages of AI methods available for such system components, it is necessary to process AI algorithms in real-time.

Innovative architectures of AI algorithms and their efficient implementation on hardware components play a central role for the professorship. Thereby, profound knowledge in the field of software parallelisation for multi-core processors is required.

The aim of the professorship is to conduct research on methods to enable the systematic use of AI methods in aeronautical engineering. The focus is on the field of "Machine Learning Control", i.e. the use of AI for the design and operation of control systems in unmanned aircraft. In manned aviation, depending on the aircraft, decisions have to be made permanently in order to operate the aircraft optimally. These decisions are to be mapped by means of artificial intelligence in order to support unmanned aviation. In the development of mobility products in particular, tests and calculations take place regularly. The use of simulation tools for calculations has already led to an optimisation of efficiency in product development. Simulation calculations and also cost calculations can indeed be based on similarities. However, in many cases the estimates required for this are still dependent on human beings. It is to be investigated how similarity considerations can be represented by means of AI and actively used in product development in order to be able to carry out product developments more quickly and with less expenditure of resources.

Job holder: Prof. Gerhard Elsbacher

The aim of the professorship is to use AI methods to work on solutions to the challenges of flight guidance for unmanned flight. The application focuses on flight management systems with increasing autonomy for classical aircraft as well as UAM. The combination of current AI methods and the research of machine learning procedures for safety-critical systems should enable efficient solutions for the following applications: Processing results of environment acquisition and replanning of the trajectory, flight guidance for fully autonomous flying for optional manned and unmanned flight systems incl. UAM as well as the integration of unmanned flight systems into the airspace.

Job holder: Prof. Dr. Christian Seidel

The use of AI requires social acceptance. Therefore, the societal implications in terms of technology assessment of AI applications have to be investigated. In this context, an ethical consideration is also necessary. The lack of transparency and explanation of automatic decisions or recommendations made by AI algorithms is critical. This societal and ethical view on the consequences of the use of AI in turn results in requirements for the development and testing of mobility systems and smart services based on AI procedures. The aim of the professorship is to conduct research at the interface between technical and societal implications of the use of AI in mobility systems and to develop basic principles for the social acceptance of AI mobility applications.

Job holder: Prof. Dr. Matthias Uhl 

The aim of the professorship is to work on methods to make passenger and freight transport more efficient, sustainable and comfortable. The focus is on AI-supported optimisation and interlocking of classic mobility offers (e.g. public transport with individual transport), individual mobility concepts (e.g. sharing offers) and parking and energy supply infrastructure (e.g. charging stations). In particular, smart services for urban areas should be considered within the framework of increasing efficiency, sustainability and comfort. The focus will be on the economic, ecological and social aspects of the use of smart services. The research area will combine macroscopic and microscopic traffic observations and investigate the benefits of mobility applications also from a business perspective.

Job holder. Prof. Dr. Harry Wagner 

The professorship is designed as a central cross-sectional professorship in the AI mobility network. It will research innovative mobility concepts that are made possible through the use of AI methods with the aim of making a positive ecological, economic and social contribution. The professorship will focus on the research of sustainable business models in the field of AI-based mobility and their application by start-ups or established companies (intrapreneurs).

The professorship will assume a comprehensive coordination role by researching the applicability of research results in the entire mobility network. It is networked with all other research professorships of the node and endpoints. Due to its innovation and organisational relevance, the professorship is to assume the necessary coordination role in the network of the AI node and the AI endpoints, for example in the development of cross-university use cases and flagship projects or the organisation of subject-specific scientific exchange formats.

The endowed chair supports the goals of the application cluster "AI-supported automotive production" and focuses on the optimization of logistics and production processes through AI-supported analyses and forecasts.

Job holder: Prof. Dr. Jürgen Bock 

The endowed chair of the city of Ingolstadt „Nachhaltige Stadtentwicklung und Künstliche Intelligenz“ addresses on the one hand prerequisites in the infrastructure, which are necessary for automated driving and autonomous flying, and on the other hand the macroscopic view of traffic, with the aim of optimizing the traffic flow by means of data-based procedures. Another focus of the professorship is the research of AI methods for mobility and infrastructure planning in urban areas as fundamental tools for the sustainable design of urban living space.

Job holder: Prof. Dr. Stefanie Schmidtner 

The self-financed professorship for "Text and Speech Comprehension" is intended to contribute to an optimized human-machine interface. The research work focuses on AI methods for processing time series such as recurrent neural networks, but also on new possibilities for semantic description of language. Architectures of AI speech processing systems for speech recognition and speech modelling will be investigated. It is to be expected that in the future a substantial part of the communication between humans and machines will take place via speech. Thus, this professorship also provides central interfaces to the three application clusters of the AI mobility node.

Job holder: Prof. Dr. Munir Georges

The self-financed professorship "Computer Vision for Intelligent Mobility Systems" focuses on advanced AI methods in the field of image processing. Special attention is paid to boundary conditions that have to be considered when using cameras in mobility vehicles, such as adverse environmental conditions or high dynamics. The aim is to investigate AI architectures that can reliably guarantee a high detection quality from camera data even under such boundary conditions. Because cameras are among the most important sensors in the realization of automated driving, unmanned flight and condition monitoring in automotive production, this professorship has central interfaces to the researchers in the three application clusters of the AI mobility node.

Job holder: Prof. Dr. Torsten Schön

The technically sophisticated systems in aviation continue to gain in complexity due to developments towards autonomous flight. As a result, the amount of software and AI procedures in the systems is increasing sharply and the operating states must be monitored more comprehensively. In order to master this increasing complexity and to meet the very high demand for quality and reliability, methods of model-based system engineering and research into suitable procedures for modeling systems that contain data-based AI components are necessary. The AIRBUS Endowed Chair Model-based Systems Engineering is dedicated to these tasks at the interface between engineering sciences and computer science.

Job holder: Prof. Dr. Stefan Kugele 

Imaging procedures, from static X-ray images to moving ultrasound and live endoscopy images to multi-layered CT and MRI images, are standard in medical diagnostics today. At the same time, new methods of AI for image analysis offer enormous potential to make diagnostics more efficient and effective, and thus to derive therapies that are more precisely tailored. Therefore, the endowed professorship of Ingolstadt Hospital investigates possibilities, success factors and limits of AI image analysis methods in medical fields of application.

Job holder: Prof. Dr. Marc Aubreville 

In the field of life sciences, automated, sensor-based data acquisition of performance and vital data such as number of steps, heart and respiratory rate or blood glucose levels plays a central role. The endowed professorship of Ingolstadt Hospital addresses possibilities, success factors and limitations of new methods of biomechanics for data acquisition. In addition, research is being conducted into which AI methods can be used to analyze the sensor data obtained in an optimal manner. The focus ranges from medical and medical-related fields of application to the "quantified self" sports and leisure sector.

Job holder: Prof. Dr. Marion Menzel 

Retailers and manufacturers alike face the challenge of selling their products to end customers in increasingly saturated markets. Therefore, it is necessary, for example, to improve the shopping experience in all channels (e.g., stationary, digital) for customers and, in addition, to optimize procurement logistics to meet dynamic demands through good forecasting. In these fields, data-based AI methods offer new approaches for optimization, the potential of which will be analyzed by the MediaMarkt Endowed Professorship in concrete application fields. The acceptance of the use of AI in this area will also be monitored and evaluated.

Job holder: Prof. Dr. Michael Jungbluth