Virtual training systems provide highly realistic training environments for police. This study assesses whether a pain stimulus can enhance the training responses and sense of the presence of these systems. Police officers (n = 219) were trained either with or without a pain stimulus in a 2D simulator (VirTra V-300) and a 3D virtual reality (VR) system. Two (training simulator) × 2 (pain stimulus) ANOVAs revealed a significant interaction effect for perceived stress (p =.010, ηp2 =.039). Post-hoc pairwise comparisons showed that VR provokes significantly higher levels of perceived stress compared to VirTra when no pain stimulus is used (p =.009). With a pain stimulus, VirTra training provokes significantly higher levels of perceived stress compared to VirTra training without a pain stimulus (p <.001). Sense of presence was unaffected by the pain stimulus in both training systems. Our results indicate that VR training appears sufficiently realistic without adding a pain stimulus. Practitioner summary: Virtual police training benefits from highly realistic training environments. This study found that adding a pain stimulus heightened perceived stress in a 2D simulator, whereas it influenced neither training responses nor sense of presence in a VR system. VR training appears sufficiently realistic without adding a pain stimulus.
This paper presents how the application of the STPA method might support the evaluation of fighter pilots training programs and trigger procedural and technological changes. We applied the STPA method by considering the safety constraints documented in the Standard Operating Procedures (SOPs) of a South European Air Force and regard a flight of a two F-16 aircraft formation. In this context, we derived the control actions and feedback mechanisms that are available to the leader pilot during an Aircraft Combat Maneuver (ACM) mission, and we developed the control flow diagram based on the aircraft manuals. We compared the results of each analysis step with the respective flight training program, which is based on a mixed skill and rule-based decision-making, and we examined the role of the feedback mechanisms during multiple safety constraints violations. The analysis showed that: the flight training program under study does not structurally include cases of infringement of multiple safety constraints; the maintenance of some safety constraints are not supported by alerts, or rely on only one human sense; the existing procedures do not refer to the prioritization of pilot actions in cases of violation of multiple safety constraints; operation manuals do not address the cases of possible human performance deterioration when simultaneous information from feedback mechanisms is received. The results demonstrated the benefits of the STPA method, the application of which uncovered various inadequacies in the flight training program studied, some of them related to the F-16 cockpit ergonomics. The analysis lead to recommendations in regard to the amendment of the corresponding fighter pilots training program, and the conduction of further research regarding the aircraft – pilot interaction when multiple safety constraints are violated. The approach presented in this paper can be also followed for the (re)evaluation of flight training schemes in military, civil and general aviation, as well by any human-machine interface intensive domain.
The five papers in the DRS 2022 track “AI and the Conditions of Design: Towards A New Set of Design Ideals” offer radical lenses to change the narrative around AI and open pathways towards pluralist digital futures, signaling redirections for experimenting with more inclusive and imaginative design practices.
In recent years, disasters are increasing in numbers, location, intensity and impact; they have become more unpredictable due to climate change, raising questions about disaster preparedness and management. Attempts by government entities at limiting the impact of disasters are insufficient, awareness and action are urgently needed at the citizen level to create awareness, develop capacity, facilitate implementation of management plans and to coordinate local action at times of uncertainty. We need a cultural and behavioral change to create resilient citizens, communities, and environments. To develop and maintain new ways of thinking has to start by anticipating long-term bottom-up resilience and collaborations. We propose to develop a serious game on a physical tabletop that allows individuals and communities to work with a moderator and to simulate disasters and individual and collective action in their locality, to mimic real-world scenarios using game mechanics and to train trainers. Two companies–Stratsims, a company specialized in game development, and Society College, an organization that aims to strengthen society, combine their expertise as changemakers. They work with Professor Carola Hein (TU Delft), who has developed knowledge about questions of disaster and rebuilding worldwide and the conditions for meaningful and long-term disaster preparedness. The partners have already reached out to relevant communities in Amsterdam and the Netherlands, including UNUN, a network of Ukrainians in the Netherlands. Jaap de Goede, an experienced strategy simulation expert, will lead outreach activities in diverse communities to train trainers and moderate workshops. This game will be highly relevant for citizens to help grow awareness and capacity for preparing for and coping with disasters in a bottom-up fashion. The toolkit will be available for download and printing open access, and for purchase. The team will offer training and facilitate workshops working with local communities to initiate bottom-up change in policy making and planning.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
