Dynamic Situational Awareness
Developing new methods to provide optimal situational awareness for humans and machines to understand each other’s situation as events evolve within dynamic missions; therefore, to collaborate effectively.
The challenge
In order to work together effectively, humans and machines need to understand each other’s situation in an optimal manner (not too high or not too low). For example, a human working with a semi-autonomous vehicle needs a good understanding of where the vehicle is, its direction and speed of travel, its surroundings, and so on. Similarly, artificially intelligent systems must know how much information the human operator has and their needs, in order to make good decisions. Human operators must neither be kept in the dark nor overloaded with information if their expertise is to be properly used. Many recent approaches to delivering situational awareness in human-machine systems focus overly on reducing the information overload (‘cognitive loading’) problem. However, this often leads to “dumbing down” or marginalising the human’s role.
Our response
This project takes on the challenge of developing new methods to provide dynamic situational awareness, changing the amount of information, what is presented, and how it is presented as a response to the user’s current state, the machine’s state, and mission progress. We research innovative ways to enable dynamic situational awareness, applied and demonstrated in human-robot teams operating in unstructured complex environments (e.g., search & rescue, agriculture). We consider how the state of the user, robots and the mission collectively affect the user’s situational awareness. We also develop strategies for communication between the human operator and the robots for optimal situational awareness and performance.
Impact
Optimal dynamic situational awareness allows humans to work with machines to boost human capabilities and enhance human decision making and performance, an important step towards the realisation of CINTEL “superteams” which combine human and machine intelligence.
Postdoc projects
Student projects
Publications
Hashini Senaratne, Alex Pitt, Fletcher Talbot, Peyman Moghadam, Pavan Sikka, David Howard, Jason Williams, Dana Kulic, and Cecile Paris, “Measuring Situational Awareness Latency in Human-Robot Teaming Experiments,” 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2624-2631, doi: 10.1109/RO-MAN57019.2023.10309377.
Stine Johansen, Hashini Senaratne, Alan Burden, Melanie Mcgrath, Claire Mason, Glenda Amayo Caldwell, Jared Donovan, Andreas Duenser, Matthias Guertler, David Howard, Yanran Jiang, Cecile Paris, Markus Rittenbruch, and Jonathan Roberts. 2023. Empowering People in Human-Robot Collaboration: Why, How, When and for Whom. In Proceedings of the 35th Australian Conference on Human-Computer Interaction (OzCHI ’23). Association for Computing Machinery, New York, NY, USA. [To Appear]
Callum Bennie, Bridget Casey, Cecile Paris, Dana Kulic, Brendan Tidd, Nicholas Lawrance, Alex Pitt, Fletcher Talbot, Jason Williams, David Howard, Pavan Sikka, and Hashini Senaratne. 2023. Alternative Interfaces for Human-initiated Natural Language Communication and Robot-initiated Haptic Feedback: Towards Better Situational Awareness in Human-Robot Collaboration. In Proceedings of the “Empowering People in Human-Robot Collaboration: Why, How, When, and for Whom” workshop at 35th Australian Conference on Human-Computer Interaction (OzCHI ’23). New Zealand. https://doi.org/10.48550/arXiv.2401.13903
Stine Johansen, Hashini Senaratne, Alan Burden, David Howard, Glenda Amayo Caldwell, Jared Donovan, Andreas Duenser, Matthias Guertler, Melanie Mcgrath, Cecile Paris, Markus Rittenbruch, and Jonathan Roberts. 2023. Empowering People in Human-Robot Collaboration: Bringing Together and Synthesising Perspectives. In Proceedings of the 34th Australian Conference on Human-Computer Interaction (OzCHI ’22). Association for Computing Machinery, New York, NY, USA, 352–355. https://doi.org/10.1145/3572921.3572955
