Emergency vehicles (EVs) are at high risk of accidents during emergency driving. To make use of countermeasures to mitigate these risks, it is important to understand under what circumstances EV-related accidents occur. The common risk factors for EV-related accidents were examined through a systematic literature review. A total of 22 articles were examined for risk factors associated with EV-related accidents. The most reported risk factors were, in order of frequency, intersections, daytime, dry roads, clear weather, urban roads, traffic signals, and angular collisions. The articles were also reviewed for suggested countermeasures to mitigate the risk factors. The most commonly suggested countermeasures were driver training for EV operators, educating the public, exercising caution at intersections, wearing a seatbelt, and intelligent vehicle technologies. Cooperative intelligent transport systems (C-ITSs) have the potential to mitigate the risks of EV-related accidents. Therefore, three C-ITS services were investigated: EV approaching, EV preemption, and geofencing. They could all be used to inform, warn, or control aspects of driving. Each suggested service has the potential to decrease risk factors for EV-related accidents. The current literature review provides guidance on under what circumstance and in what form C-ITSs could be beneficial to prevent EV-related accidents. Further research is needed to examine behavior when drivers are introduced to C-ITSs.

A pilot project with three substudies (a literature survey, a workshop, and a field experiment at test track) is reported. The intent is to develop realistic and prototypical driving scenarios with associated patient-care tasks, for use in ambulance-simulator studies and training of ambulance personnel, in a future project. The goals were to survey the current knowledge and to collect relevant data on vehicle dynamics and behaviour during simulated emergency scenarios. Prior research has foremost studied the quality of CPR (cardiopulmonary resuscitation) during transport and shown that it is difficult to perform high-quality CPR depending on the transport procedure, vehicle type, and the design of the care space. From the workshop, care interventions were chosen for the subsequent field experiment; based on frequency and difficulty. Factors that affect the care interventions during transport were categorised as motion related, design related, road surface status, and cooperation. The field experiment at a test track showed that workload and difficulty in performing the care interventions were rated higher when the transport entailed more and faster speed changes and turns. Motion-related factors was the highest-rated category with regard to effect on care interventions.