Attacks on vehicles through the use of improvised explosive devices (IEDs) and the incidence of vehicle rollovers have significantly increased over the past few years. It is important to quickly extract casualties from vehicles that have been targeted by these devices. The approach to extracting a casualty from a vehicle varies based on the condition and position of the vehicle, the expected injuries of the casualties inside, the position of the enemy and status of enemy fire, and the type of vehicle casualties are being extracted from. Given that the combination of these variables and many others make each vehicle extraction unique, it is essential to provide a variety of training conditions within which Combat Medics and Combat Lifesavers can practice the extraction task in order to help them develop strategies to make the best decisions possible and increase the survivability of causalities and the extraction team.

This paper describes the process and results of an effort to develop requirements for a reconfigurable vehicle casualty extraction training system that safely allows trainees to practice the knowledge, skills, and abilities needed to perform casualty extraction under a variety of realistic conditions. First, a task analysis and training needs is outlined that was used to define the extraction process and identify potential inefficiencies associated with current methods used to conduct training on this complex task. Next, a process is outlined that was used to extract the critical cues and functional requirements that are essential to integrate within the training system to target each critical extraction subtask. Finally, the design of the Vehicle Extraction (V-Xtract) training simulator that is currently being developed to meet these needs is presented. The V-Xtract system provides a safe, realistic, highly configurable environment that can efficiently present a variety of training conditions to trainees, objectively evaluate their performance, and provide guidance on how to progress future training based on past performance on targeted training objectives.