The use of aerial and ground vehicles that can operate fully autonomously or with a high level of automation requiring human intervention is increasing. They need to work in in dynamically changing, outdoor environments for transportation, logistics, and mobility. In current uncertain real-world environments, safety and resilience are ensured by appropriate situational awareness, risk awareness, coordination, and decision making by humans. Examples are pilots recognizing adverse weather conditions and informing air traffic control and other pilots about the perceived risk, or car drivers recognising nearby playing kids and adapting their driving in an effort to avoid a potential accident. In increasingly autonomous operations, the situation awareness, risk awareness and experience of human operators that have played such vital roles can no longer be counted upon. Humans create mental models to understand hazards, outlining their components, potential changes over time, potential impacts, and control measures, which are the basis of human situational awareness (SA), involving the perception of environmental elements in time and space, comprehension of their significance, and anticipation of their near-future status. Achieving and maintaining SA is crucial for safety in human-operated road and air traffic control. However, modern robotic agents must make decisions that go beyond what traditional human-based SA models can encompass.
In this context, SymAware introduces a new cognitive architecture designed to align with the internal models of robotic agents and facilitate the safe collaboration of autonomous agents and humans. SymAware's primary objective is to provide a comprehensive framework for situational awareness in multi-agent systems, enabling agents to actively perceive risks in evolving environments and collaborate with robots and humans for complex, dynamic tasks. This framework encompasses various awareness dimensions, including knowledge, spatiotemporal, risk, and social aspects, using logic, symbolic computations, formal reasoning, and uncertainty quantification to achieve full situational awareness in multi-agent systems.
