IntelliMar Intelligent assistance and analysis systems for early detection and management of maritime hazardous situations


In times of growing traffic volumes at sea, monitoring maritime safety within German territorial waters is a central prerequisite for ensuring integrated freights logistics which are important for exports. Digitalization is an important measure to achieve more effective monitoring with manageable costs and risks.

The number and quality of available sensors for the worldwide recording of ship movements and environmental data in maritime space is constantly increasing. As a result, the possibilities for early detection of abnormal behavior, for example of ship movements, are improving. The previously widely used process engineering approaches to data analysis are progressively and more and more frequently reaching their limits here – despite increasing computing capacities.

In this context, it is necessary to improve maritime safety in the North and Baltic Sea through automatic anomaly detection and risk indication. The focus is on data analysis based on artificial intelligence as well as on usability and system ergonomics.


The overall goal of the IntelliMar project is to analyze new concepts and future services in the field of intelligent data analysis within the framework of industrial research, and to contribute to a significant improvement of existing concepts and processes. Thus, the project is part of a holistic view of an innovative overall architecture for the increasingly complex requirements of maritime use cases – especially with regard to maritime safety. The research assignments primarily comprise an innovative sub-aspect of an overall architecture.

The situation awareness of decision-makers and task forces in crisis or dangerous situations is to be improved by providing them with reliable and situation-specific information at the right time in a better way than before (higher degree of digitalization and automation). This approach supports users both in decision-making and in the implementation of measures, and ultimately serves to increase the response capability and protection of the personnel involved.

One sub-project of IntelliMar deals with the Distributed Cooperative System (DSC), which is used to capture, model and evaluate decision situations in maritime threat scenarios. A DSC consists of a number of human and machine agents that have certain cooperative relationships with each other and work together on higher-level tasks.

The agents are assigned subtasks according to their powers, available resources, and agent-specific capabilities, which together contribute to the fulfilment of the overall task. Examples of agents are maritime operators with specific tasks, service providers who develop and provide systems for processing such sensor data, or machine systems in the form of intelligent and rule-based assistance and analysis systems that derive high-quality and thus more useful data (smart data) from the available mass data (big data). The modelling of decision problems in maritime threat scenarios as DSC provides a formal and executable basis for the optimal distribution of subtasks as well as information and knowledge to the agents involved. In this way, decisions are optimally prepared depending on the dynamically changing threat situation by supporting the situation awareness of each agent adapted to his tasks.


Maritime Sensor Systems (AIS, Satellite data), Human Factors Engineering, Machine Learning, Simulation.


External Leader

Sonja Sievi, Airbus Defence and Space GmbH: Space Systems, Bremen

Scientific Director

Towards Improving Situation Awareness of Maritime Field Operators Using Augmented Reality

Saager, Marcel and Behrendt, Marcus and Baber, Patrick; HCI International 2021 - Late Breaking Papers: Multimodality, eXtended Reality, and Artificial Intelligence; 2021

Airbus Defence & Space GmbH
HuMaTects GmbH
DLR - Deutsches Fernerkundungsdatenzentrum (DFD)
DLR Institut für Kommunikation und Navigation


Start: 31.08.2018
End: 27.02.2021

Source of funding

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