Using a Game Engine to Simulate Critical Incidents and Data Collection by Autonomous Drones
This work addresses the need for safe and controlled training of AI systems for emergency responders in hazardous scenarios, though it is incremental as it builds on existing simulation methods.
The researchers tackled the problem of developing and testing autonomous drone systems for critical incident response by creating a virtual environment using a game engine to simulate CBRNe events, enabling rapid prototyping and validation of AI technologies for evidence collection and scene assessment.
Using a game engine, we have developed a virtual environment which models important aspects of critical incident scenarios. We focused on modelling phenomena relating to the identification and gathering of key forensic evidence, in order to develop and test a system which can handle chemical, biological, radiological/nuclear or explosive (CBRNe) events autonomously. This allows us to build and validate AI-based technologies, which can be trained and tested in our custom virtual environment before being deployed in real-world scenarios. We have used our virtual scenario to rapidly prototype a system which can use simulated Remote Aerial Vehicles (RAVs) to gather images from the environment for the purpose of mapping. Our environment provides us with an effective medium through which we can develop and test various AI methodologies for critical incident scene assessment, in a safe and controlled manner