We are a team of researchers strongly dedicated to providing high-quality results to our partners, customers, and sponsors.
Competence
and Research Focus
Machine Learning
We are experts in Artificial Intelligence and Machine Learning and continue to explore related research topics and their application for Artificial Situation Awareness.
Autonomous Systems
Situation Awareness is a key aspect of human intelligence and a prerequisite for truly Autonomous Systems.
Assistance Systems
To support and assist a user in all situations in the best possible manner requires the Situation Awareness capability.
System Safety
Applying Autonomous and Assistance systems in real-world scenarios requires consideration of safety aspects throughout the entire system’s lifetime.
Adjacent Technologies and Applications
Our application areas and domains are widely distributed, from certifiably safe autonomous systems over evacuation assistance systems to health care systems.
Portfolio
Ongoing and past projects (selection)
Evacuation assistance system
Improving team situation awareness in complex scenarios
Situation Awareness-based Attention Guidance
Attenion Guidance in Augumented Reality applications
Situational Risk Assessment
Safe decision making through situational Risk Assessment
Supervisor-based Safe Behavior Assurance
Supevirory safety unit to ensure overall system's safe behavior
Understanding Implicit Object Relationships for Enhanced Situation Awareness in AI Systems
Unveiling Subtle Connections to Elevate AI Perception and Response
Safety Assurance of ML-Based Algorithms
Mitigating Risks and Ensuring Reliability in AI System
Evacuation assistance system
This project aims to design a practical evacuation assistance system for evacuation leaders and teams to improve their situation awareness in emergencies through a mobile application.
Evacuating the buildings during various emergencies, such as fire or terrorist attacks, requires high awareness of the environment, quick decision-making, and immediate action.
This project aims to design a practical evacuation assistance system for evacuation leaders to improve their situation awareness in emergencies through a mobile application.
Furthermore, a proof of concept is considered a part of this project. Using the proposed system, the leaders will be aware of their situations in real-time, such as environmental hazards, evacuation procedures, extra assistance requests.
Accordingly, a proper sensory infrastructure will be proposed to support indoor positioning in the building. This system’s primary goal is to improve the situation awareness of an evacuation team which composed of several individual evacuation leaders.
Accordingly, a team situation awareness module will be proposed as a part of assistance system.
However, both evacuation leaders and occupants can be users of the application. The occupants can use the application to inform the evacuation leaders about their status and be tracked by the leaders if it is necessary.
The focus of this application is the evacuation of buildings with large spaces and large numbers of occupants, such as schools, business buildings, and shopping malls, in emergencies.
Date:
Contributors: A. Sarkheyli-Hägele, J. Holmberg, R. Malekian, J. Persson. G. Hägele
Situation Awareness-based Evacuation Assistance System
IEEE World Forum on Public Safety Technology
2024
Situation Awareness-based Attention Guidance
Situation Awareness-based Attention Guidance (SAAG) is aimed at developing a novel Situation Awareness component (SAc) for an Attention Guidance system to improve human situation awareness in complex environments and tasks.
Situation Awareness-based Attention Guidance (SAAG) aims to develop a novel Situation Awareness component (SAc) for an Attention Guidance system to improve human situation awareness in complex environments and tasks.
The Situation Awareness-based Attention Guidance (SAAG) system, focusing on SAc, is realized and evaluated for the players of an Augmented Reality (AR) tabletop game. Situation awareness is crucial for decision-making and action-taking.
The awareness emerges from relating knowledge about the environment, human goals, and objectives. Accordingly, the SAc will mimic this process artificially through a novel integration of four functional modules responsible for perception, situation recognition, intention recognition, and projection.
The SAc outputs a situation-action space considering the user preferences, goals, and intentions used as input for decision-making.
The project aims for both short-term scientific and long-term industrial and societal impact. It is revealed as a human assistance system prototype for complex situations.
In addition, it has concrete contributions to artificial situation awareness to be published in academic venues covering human-computer interaction, situation awareness, and AR games.
The most immediate societal impact of the developed SAc will probably be embedded in persuasive technologies for reaching long-term behavioral change for individuals and society in the areas of healthcare, safety, and climate change.
Date:
Contributors: A. Sarkheyli-Hägele, J. Font, D. Kadish, T. Pederson, G. Hägele
Towards Situation Awareness and Attention Guidance in a Multiplayer Environment using Augmented Reality and Carcassonne
2022 Annual Symposium on Computer-Human Interaction in Play
2022
Situational Risk Assessment
Situational risk assessment in open and non-predictive environments to support decision making and ...
… action selection for autonomous systems is a challanging task and focus of this project.
Decision making and action selection incorporate decreasing situational risks and maintain safety as operational constraints.
Commonly, neither existing safety standards nor the situation modeling or knowledge representation is considered in that context.
This project investigates and applies novel approaches related to Safety-Driven Behavior Management for different applications like unmanned aerial systes or automated driving. The focus lies mainly on situation modeling, and the problem of knowledge representation in the context of situational risks.
One of the research questions is whether the safety standards-oriented hazards analysis and the risk assessment approach can be combined with the machine learning-based situation recognition.
Date:
Contributors: G. Hägele, A. Sarkheyli-Hägele
Related Publication:
Author
Title
Publisher
Year
G. Hägele, A. Sarkheyli-Hägele
Situational risk assessment within safety-driven behavior management in the context of UAS
The 2020 International Conference on Unmanned Aircraft Systems
2020
G. Hägele, A. Sarkheyli-Hägele
Situational hazard recognition and risk assessment within safety-driven behavior management in the context of automated driving
IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
2020
G. Hägele, A. Sarkheyli-Hägele
Situational Decision Making Using Situation Modeling and Deep Learning
IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
2021
Supervisor-based Safe Behavior Assurance
Supevirory safety unit to ensure overall system's safe behavior
Autonomous or semi-autonomous systems are used in different application domains to simplify or assist human tasks.
In this context, their behavior has to be verifiably safe. This project focuses on techniques for specifying and designing a supervisory safety unit to ensure the overall system’s safe behavior.
This includes methods for run-time risk area generation and adjustment, situational hazard analysis and risk assessment, run-time technical system reliability assessment, and emergency behavior modeling are investigated.
Date: 2013 - 2017
Contributors: G. Hägele
Related Publication:
Author
Title
Publisher
Year
G. Hägele, D. Söffker
Risk Areas Determination for Autonomous-and Semi-autonomous Aerial Systems Considering Run-Time Technical Reliability Assessment
Journal of Intelligent & Robotic Systems
2019
G. Hägele, D. Söffker
Safety unit-based safe behavior assurance for autonomous and semi-autonomous aerial systems: Requirements, concept, and simulation results
IEEE Intelligent Vehicles Symposium
2017
G. Hägele, D. Söffker
System safety surveillance and control system concept for autonomous or semi-autonomous systems fall-back layer realization
Dynamic Systems and Control Conference
2016
G. Hägele, D. Söffker
Strictly Formalized Situation-Operator-Modeling technique for fall-back layer modeling for autonomous or semi-autonomous systems requiring software-based fail-safe behavior
IEEE International Conference on Systems, Man, and Cybernetics (SMC)
2016
G. Hägele, D. Söffker
Fall-back layer concept for autonomous or semi-autonomous systems and processes: requirements, concepts, and first tests
IEEE International Conference on Systems, Man, and Cybernetics (SMC)
2016
Understanding Implicit Object Relationships for Enhanced Situation Awareness in AI Systems
Unveiling Subtle Connections to Elevate AI Perception and Response
In this dedicated project, our aim is to bolster artificial situation awareness by delving into the realm of implicit object relationships. While explicit relationships between objects are often readily discernible, it is the implicit connections that often hold the key to a deeper understanding of complex situations. Our research focuses on developing algorithms and methodologies to uncover these subtle links, thereby enriching the perception capabilities of AI systems.
Using a combination of machine learning techniques, data analysis, and contextual understanding, we embark on a comprehensive investigation.
Date: 2024 - ongiong
Contributors: A. Sarkheyli-Hägele, G. Hägele
Related Publication:
Author
Title
Publisher
Year
G. Hägele, A. Sarkheyli-Hägele
Situational Decision Making Using Situation Modeling and Deep Learning
IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)
2021
Safety Assurance of ML-Based Algorithms
Mitigating Risks and Ensuring Reliability in AI System
This project is dedicated to tackling the crucial challenge of guaranteeing the safety and reliability of machine learning (ML) algorithms across diverse applications.
With ML algorithms becoming integral to sectors like healthcare, machinery, and transportation, ensuring their safety is essential for widespread adoption, trust, and legal compliance.
Our research delves into advanced methodologies, including uncertainty estimation and conformal prediction, to enhance ML algorithms’ monitoring and runtime verification.
We expect continuous monitoring of algorithmic performance and behavior to enable the detection of anomalies and safety-critical deviations.
This proactive approach will allow timely interventions to uphold systems’ safety and reliability, thus bolstering trust in ML systems and their use within safety-critical applications.
Date: 2023 - ongiong
Contributors: G. Hägele, A. Sarkheyli-Hägele
Related Publication:
OUR TEAM
Arezoo Sarkheyli-Hägele
Director
Georg Hägele
Asossiated Director
Magnus Johnsson
Academic Advisor
Johan Holmberg
Senior Researcher
Erdal Akin
Senior Researcher
Raoof Doorashi
Project Assistant
Md Mahbubur Rahman
Project Assistant
Erik Svensson
Research Assistant
Alberto Biscalchin
Research Assistant
External collaborators
Ahmad P. Tafti
University of Pittsburgh, Pitt Health + Explainable AI (Pitt HexAI) Research Laboratory
