feir

Leader training is a fundamental pillar of fire and rescue services. It is crucial for the success of operations – and thus for protecting and saving human lives. During the initial investigation at the emergency scene, the situation must be assessed within seconds, and decisions must be made. Training courses for these situations are currently held at extended intervals in the state firefighting schools. Simulation games with miniatures of scenarios and model vehicles are common practice here. As the training takes place from a bird’s-eye view, it cannot match the reality of the operation. On this account, developing new and innovative training methods is essential to make training more realistic and practice-orientated, thus improving the leaders’ skills. Virtual training methods and Artificial Intelligence can generate customized scenarios to prepare firefighters for realistic operational situations.

The project aims to develop a virtual training platform for educating leaders in emergency response, such as during the fire service team leader training. In contrast to previous training methods with static training scenarios, feir is designed to offer a new and customized learning experience. The feir project uses Artificial Intelligence to generate customized training scenarios in a virtual environment. Hence, leaders can practice according to their skills and learning progress in a realistic environment. The generated scenarios can prepare them for specific situations from the initial investigation throughout the operation. Structured evaluations provide them with rapid feedback, enabling them to improve their learning success.

In the project, the FZI focuses on developing an automated evaluation of processes in the virtual simulation environment for the leadership training of rescuers. To this end, the practice sessions in virtual reality will be automatically analyzed, and objective evaluation standards will be developed. The results will be communicated to the participants using learning theory approaches. Furthermore, the insights gained will automatically translate into subsequent training scenarios customized to the participants’ learning progress.