Sveriges civila försvar befinner sig i en snabb utvecklingsfas där räddningstjänstens roll under höjd beredskap (RUHB) får allt större betydelse. Det aktuella arbetet har genomförts inom ramen för MSB:s RUHB-uppdrag och syftade till att utvärdera hur obemannade system – framför allt fjärrstyrda obemannade luftfarkoster och markfordon (UAS och UGV) – kan öka säkerhet, effektivitet och uthållighet i räddningstjänstens insatser. Under ett större metodförsök i Kiruna 2025 testades ett flertal plattformar i realistiska scenarier för att bedöma deras operativa nytta.

Resultaten visade att obemannade system har stor potential som förmågehöjande verktyg, särskilt för informationsinhämtning och riskbedömning i farliga miljöer. Drönare och markrobotar kan snabbt skapa en nulägesbild och minska exponering för risker. Samtidigt identifierades ett antal utvecklingsområden som behöver adresseras för att tekniken ska kunna införas brett. Bland dessa märks behovet av förbättrad informationsdelning, både tekniskt (t.ex. strömning av video till ledningssystem) och organisatoriskt (tydliga mottagare och rutiner), tillgång till systematiska tester och nationella testmiljöer för att jämföra plattformarnas prestanda och robusthet under svenska förhållanden, samt vägledning för när tekniken är mogen att användas i skarpa insatser. Det bör även utredas vilka mätinstrument och moduler för CBRNE-detektion som kan användas, och vilken nytta mindre, enklare robotar kan ge vid datainsamling i svåråtkomliga miljöer eller för att skapa kommunikationsvägar.

En viktig strategisk slutsats är att Sverige bör bygga vidare på de tydliga styrkor som redan finns – framför allt snabba, små och kostnadseffektiva plattformar – samtidigt som en tydlig nulägesbild över hur räddningsrobotar används i dag hos svensk räddningstjänst bör skapas, samt att ett nätverk för kunskaps- och erfarenhetsdelning om räddningsrobotik bör etableras. Detta skulle möjliggöra gemensamma standarder, stärka operatörernas kompetens och på sikt bidra till en mer robust och teknikstödjande räddningstjänst under höjd beredskap. Slutligen bör det inom RUHB utredas vad som ska utgöra basförmåga, förstärkt förmåga och nationell resursförmåga samt hur dessa bäst kan organiseras och utrustas.

Young drivers are a high-risk group for road traffic accidents, highlighting the need for advanced methods to assess and improve risk awareness. With driving simulators being considered as a possible mandatory part of the Swedish driving test, this study examines the effects of different weather conditions on performance and perceived risk during simulator-based driving tests.

26 participants (mean age = 19, SD = 2, 12 male, 11 female, and 3 unspecified) mainly recruited at an automotive high school took part in the study. All participants completed 14 driving situations in a fixed-based simulator. Weather conditions—daytime, nighttime, rain, and fog—were varied in a rolling scheme, with 3–4 traffic situations per condition. Participants, divided into four groups, experienced all weather conditions. 

Outcomes were recorded as pass/fail and supplemented with self-assessment ratings of performance and perceived risk for each situation. There was a statistically significant effect of weather conditions on driving performance, F(3, 69) = 2.973, p < .05. The test performance score (M ± SD) was highest in daytime (.64 ± .37) and nighttime (.64 ± .34) followed by rain (.58 ± .38), and fog (.43 ± .40). However, post hoc analysis with a Bonferroni adjustment indicated no statistically significant pairwise differences. There was no statistically significant effect of weather conditions on self-assessed driving performance or on perceived risk. This study offers new insights into using light and weather conditions in simulator-based driver assessment.

Automated shuttle buses (ASBs) hold significant potential for improving public transportation by enhancing urban mobility and sustainability. However, public trust in ASB and people’s intention to use them may depend on factors such as age and expectations of ASB behavior. This study examined how trust in ASBs’ capabilities influences the intention to use them and expectations of their braking behavior, specifically from the perspective of pedestrians and cyclists. Additionally, the study explored how age affects this relationship. Five hundred participants completed an online questionnaire assessing trust and behavioral intention to use ASBs. Participants then viewed video scenarios depicting ASBs interacting with pedestrians and cyclists and reported their expectations regarding the ASBs’ braking responses to prevent collisions. Structural equation modeling (SEM) revealed that trust positively predicted both the intention to use ASBs and expectations of their braking performance. Age was negatively associated with trust but did not predict intention to use or braking expectations. These findings show that trust is a critical factor in ASB adoption across age groups. To foster trust and encourage the adoption of ASBs, developers, providers, and policymakers, should prioritize delivering reliable, transparent, and user-friendly information and services that demonstrate ASBs’ safety and performance.

As autonomous vehicles (AVs) emerge as new agents in traffic, it becomes imperative to understand how they can be introduced effectively and acceptably. AVs enter existing socio-technical systems, where members of various social communities are already present, including residents rushing to work, tourists strolling around, and workers cleaning the streets. This workshop seeks to explore the intricate dynamics of introducing AVs into established social systems within shared spaces. It aims to understand what the needs of pedestrians, cyclists, drivers, and passengers are, and how they interact with each other when AVs enter the social system. Through presentations and interactive activities, this workshop will provide a platform for researchers, practitioners, and other stakeholders to exchange insights, share experiences, and co-create strategies relevant to the integration of AVs into the social systems of shared spaces. The workshop aims to discuss the benefits, risks, and design challenges associated with integrating this emerging technology into our everyday lives.

People's ascription of intentional agency to robots necessitates understanding how, when, and why people attribute robot behavior to underlying intentional states. While many studies explored mind attribution to robots including its determinants and consequences, little attention has been given to the attribution of second-order mental states, such as a robot's beliefs about people's intentions during interactions. In an online study (n = 155), participants watched a video of a humanoid robot tracking a ball hidden under one of two cups. 19% of participants predicted that the robot could correctly locate the ball after it was displaced twice by a person deliberately distracting the robot, indicating implicit attribution of second-order beliefs to the robot. These implicit attributions influenced participants' actions in a subsequent interactive game with a virtual counterpart of the robot but did not affect their explicit assessments of the robot's second-order reasoning. In contrast, observing the robot demonstrate second-order reasoning by correctly identifying the ball's location affected participants' explicit attributions but not their behavior in the interactive game. This reveals a complex interplay between implicit and explicit attribution processes in how people interpret robot behavior.

Young people represent a high-risk group of drivers and the prevalence of road traffic crashes among young drivers is high. Thus, to increase traffic safety, it is essential to ensure that new drivers are both sufficiently educated in and assessed for risk awareness.The aim of this study was to examine the possibility and potential benefit of using a driving simulator screening test as a complement to the existing on-road driving test. The main idea is to detect drivers who are not ready to proceed to the driving test.A comparative study was performed with participants who passed and failed a simulator test and an on-road driving test, respectively. A comparison between subjective and objective measures of performance and risk was also included. A driving simulator was placed at a traffic school and customers were recruited as participants. In total, 70 participants took part in the study and the simulated drive consisted of rural roads, urban traffic, and motorways with 16 different scenarios, constructed from the second level of the GDE matrix, to examine driving behavior, attention, and risk perception.The results show that with a screening test in a driving simulator, it is possible to detect drivers who consider themselves ready to take a driving test, but who have not yet reached the level of risk awareness required to be a safe driver. Test scenarios should be suited to detect deficiencies in risk awareness, test different levels of the GDE matrix and, to complement the driving test, be difficult to assess in an on-road driving test. Deficiencies in self-evaluation that are well-known among young drivers are again confirmed. To practice self-evaluation, the driving simulator is suggested as a pedagogical tool, linked to the GDE matrix.

Service robots, like delivery or telepresence robots, often act as proxies for people, though this representation is not always obvious to observers. For instance, when a delivery robot cuts in line at a grocery store, the identity of the person it represents matters. The lower social status often assigned to service robots (“it’s just a robot, it can wait”) can become problematic in human-robot conflicts, if the represented individual (e.g., senior citizen) is treated unfairly. Previous research indicates that people tend to overlook the human behind the robot, potentially disadvantaging the service recipient. This study aimed to determine whether people are more likely to grant priority to a delivery robot when informed about the person it represents. In a between-subjects virtual reality (VR) experiment the recipient was either made apparent with an avatar or not and participants had to choose if they would give priority to a delivery robot in a grocery store queue. Results showed that while the presence of the avatar increased participants’ self-reported consideration of the robot’s human recipient, it did not lead to higher compliance, as the willingness to grant priority was unexpectedly high in both conditions. The results suggest that social desirability and the novelty of human-robot interaction (HRI) may explain these outcomes and future research is discussed.

Understanding how people attribute behavior to underlying mental states is crucial to the study of human social interactions. Previous research has established that people rely on attributing mental states to interpret and interact with artificial agents. For example, a pedestrian encountering a driverless vehicle at a crosswalk might view the vehicle as knowing (or not) that there is a pedestrian in front of it. Nevertheless, little attention has been devoted to investigating people's attributions of higher-order mental states, i.e., mental states that are about the mental states of others (e.g., the vehicle's beliefs about the pedestrian's intentions). Addressing this research gap, the present study conducted a survey to explore people's attributions and preferences concerning higher-order mental states in three types of artificial agents (AI chatbot, virtual assistant, and self-driving car), alongside two human agents (participants themselves, referred to as you, and five-year-old child). The survey revealed that: (1) artificial agents, in contrast to humans, may be perceived as more likely to have higher-order mental states than first-order mental states, depending on the purpose or function of the agent; (2) people may prefer some artificial agents to have mental states of a particular order (but not others); (3) attributions and preferences regarding mental states in artificial agents do not always match. The study also contributes insights regarding the methodological challenge of constructing a survey that effectively captures participants' higher-order attributions while minimizing excessive cognitive demands. We posit that human-agent interaction research and design stand to benefit from further exploration of people's attributions and preferences regarding higher-order mental states in artificial agents.