## 1 Background  
The world has experienced a paradigm shift in the mode of living with a transition from rural living to a growing urban population [1]. In 1800, approximately 3% of the world's human population lived in urban areas. However, by 1900, the urban proportion rose to approximately 14% and by 2008 the urban proportion was approximately 50% [1]. The United Nations predict that on a global basis the urban population will reach 61% by 2030 and will eventually reach a \"dynamic equilibrium of approximately 80% urban to 20% rural dwellers that will persist for the foreseeable future\" [1]. This transition presents a number of challenges which include many socio-economic, demographic, economic, cultural, and environmental considerations [1].  
The literature provides abundant examples of research into these challenges; however, the growth of urban environments presents significant problems for authorities in the effective provision of emergency services. Such services range from the ability of police, fire, and healthcare (e.g., an ambulance) to reach emergencies promptly to the provision of rescue and recovery in disaster (environmental and terrorist) scenarios. A primary concern for authorities tasked with the provision of emergency services is the ability to travel in urban areas efficiently, this is an area that has received less attention in the literature. In considering the disasters scenarios human intervention may be impractical or even dangerous. In such cases robotic systems may be employed; rescue robotics are designed to enable emergency responders [and other stakeholders] to \"sense and act at a distance from the site of a disaster or extreme incident\" [2].  
Traversing a dynamic urban environment for emergency service provision demands the identification of potential paths (routes) and the selection of the optimal routes. Such routes will be characterised by a number of factors including: (a) congestion on the road network, to (b) blocked routes in emergency disaster scenarios. In this paper introduce a novel approach which is designed to achieve autonomous emergency service vehicle (or robotic systems) control; the aim is to match the control with emergency response strategies using group decision making implemented in smart dynamic urban environments.  
The proposed model utilises a novel path planning method using a self-organizing map (SOM) in an integrated Spiral STC algorithm termed the: Hybrid SOM-Spiral STC (HSSTC) model. The model enables autonomous vehicle control using hedge algebras and _Kansei_ evaluation in group decision-support. The proposed model is designed to quantify qualitative factors using sensor data processed with human sensibilities and preferences [using linguistics and semantics] in emergency scenario decision support. The experimental results show that the proposed model achieves a significant improvement in group decision-support under dynamic uncertainty. We posit that our novel approach holds the prospect of improvements in the provision of emergency services.  
The remainder paper is structured as follows: Section 2 provides an overview of the problem with the proposed contribution. A review of related research is set out with a summary in Section 3. We consider situational awareness and pervasive conciousness in Section 4 with smart environments addressed in Section 4.1. The fog computing paradigm is introduced in Section 4.2. In Section 5 we consider materials and methods where we address the problem formation in Section 5.1 with the proposed system set out in Section 6 and emergency strategies quantified using self organising maps introduced in Section 6. Section 9 presents the results with a discussion with consideration of future directions for research. The paper closes with Section 10 where we set out concluding observations.