Autonomous systems (AS) have enormous potential and are bound to be a major driver in future economic and societal transformations. Their key trait is that they pursue and achieve their more or less explicitly defined goals independently and without human guidance or intervention. In contexts where safety or other critical properties need to be guaranteed, it is, however, hardly possible at present to exploit autonomous systems to their full potential. Unknowns and uncertainties are induced due to high complexity of the autonomous behaviors, the utilized technology, and the volatile and highly complex system context in which AS operate. These characteristics render the base assumptions of established assurance methodologies (and standards) insufficient and make it necessary to investigate new approaches at runtime/operation.

One promising approach for building dependable autonomous systems is to design such systems with the capability to identify, assess, and control risks. Implementing such Dynamic Risk Management (DRM) entails many challenges concerning the necessary self-awareness and context awareness. On the one hand, powerful and thus complex self-awareness and context awareness are necessary to minimize risks, resolve conflicting objectives, and make acceptable trade-off decisions. On the other hand, the complexity of the models hinders the assurance of critical properties and prevents gaining sufficient confidence in DRM. DRM has the potential to not only enable certain types of systems or applications outright, but also to significantly increase the performance of already existing ones. This is due to the fact that by resolving unknowns and dealing with uncertainties at runtime, it will be possible to get rid of worst-case assumptions that are typically detrimental to a system’s performance properties.

The DREAMS workshop intends to explore concepts and techniques for realizing DRM. It invites experts, researchers, and practitioners to give presentations and take part in in-depth discussions about prediction models for risk identification, integration between strategic, tactical, and operational risk management, architectures for dynamic risk management, and Validation&Verification of dynamic risk management.

DREAMS aims at bringing together communities from diverse disciplines, such as safety engineering, runtime adaptation, system reconfiguration, predictive modeling, and control theory, and from different application domains such as automotive, healthcare, manufacturing, agriculture, and critical infrastructures.

Topics of interest include, but are not limited to:

DRM concepts and methods (e.g., methods for deriving suitable risk metrics)
DRM architectures
Layered DRM approaches combining different scopes (e.g., combining DRM at the trajectory planning level and at the maneuver planning level)
Collaborative DRM performed by groups of cyber-physical systems
AI-based DRM and trustworthiness considerations
DRM classifications and taxonomies
Case studies