Evaluation of the Effects of Anxiety on Behaviour and Physiological Parameters in VR Fire Simulations

This study considers the use of virtual reality technologies both to evaluate the optimal exodus paths from buildings and to analyze the behaviour of people in emergencies through the monitoring of physiological parameters. The degree of realism with which VR devices can simulate highly anxiogenic scenarios is considered. The experimentation is carried out on a school building and tested through a pilot trial where saturation, blood pressure, heart rate as well as the time of exodus are monitored. The variations of the peak values of physiological parameters highlight the influence of anxiogenic elements in the participant's state of anxiety

Multi-agent simulations for emergency situations in an airport scenario

This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

Prospects for use of extended reality technology for ship passenger evacuation simulation

Safety of passengers on ships is usually investigated based on data available from post-accident reports, experimental research and/or numerical modelling of emergencies. As for the numerical modelling, ship passenger evacuation falls within a greater set of pedestrian evacuation research in which extended reality (XR) technology is playing important role lately. However, XR still strives to find its place in the modelling of ship passenger evacuation. This paper brings review of literature published on the topic of XR in pedestrian evacuation with special focus on the use of these technologies (e.g. virtual reality, augmented reality) in shipping industry. Findings are put in the context of IMO’s guidelines for evacuation analysis and prospect for use of XR for ship passenger evacuation simulation are presented

Use of a Laser Scanning System for Professional Preparation and Scene Assessment of Fire Rescue Units

The paper presents results of a study focused on usability of a 3D laser scanning system by fire rescue units during emergencies, respectively during preparations for inspection and tactical exercises. The first part of the study focuses on an applicability of a 3D scanner in relation to an accurate evaluation of a fire scene through digitization and creation of virtual walk-through of the fire scene. The second part deals with detailed documentation of access road to the place of intervention, including a simulation of the fire vehicle arrival

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis