This dissertation investigated the effects of spatialized auditory cues on the development of situation awareness for teams. Based on extant research, it was hypothesized that 3-D spatialized auditory cues can be utilized by teams to develop knowledge about team member location in addition to supporting the usage of team behaviors for developing and maintaining situation awareness. Accordingly, the study examined how situation awareness would be differentially influenced by varying the type of auditory cues incorporated into virtual environment (VE) team training scenarios within the context of a MOUT team task. In general, the results of this study provided partial support for the beneficial effects of 3-D audio cues in facilitating the development of situation awareness and reducing workload. Implications are discussed in the context of design guidance for VE training systems.

Unmanned Aerial Vehicles (UAV), often referred to as drones, have seen increased use in search and rescue (SAR) missions. Traditionally, these missions involve manual control of each drone for aerial surveillance. As UAV autonomy progresses, the next phase in drone technology consists of a shift to autonomous collaborative multi-drone operations, where drones function collectively in swarms. A significant challenge lies in designing user interfaces that can effectively support UAV pilots in their mission without an overload of information from each drone and of their surroundings. This thesis evaluates important human factors, such as situational awareness (SA) and cognitive workload, within complex search and rescue scenarios, with the goal of increasing trust in multi-drone systems through the design and testing of various components. Conducting these user studies aims to generate insights for the future design of multi-drone systems. Two prototypes were developed with a multi-drone user interface, and simulated a stressful search and rescue mission with high cognitive workload. In the second prototype, a heatmap guided UAV pilots based on the lost person model. The prototypes were tested in a conducted user study with experienced UAV pilots in different SAR organizations across Sweden. The results showed variability in SA while monitoring drone swarms, depending on user interface components and SA levels. The prototypes caused significant cognitive workload, slightly reduced in the heatmap-equipped prototype. Furthermore, there was a marginal increase in trust observed in the prototype with the heatmap. Notably, a lack of manual control raised challenges for the majority of participants and many desired features were suggested by participants. These early expert insights can serve as a starting point for future development of multi-drone systems. / The HERD project, supported by the Innovation Fund Denmark for the DIREC project (9142-00001B)

Predicting the future state occupancies of pedestrians in urban scenarios is a challenging task, especially considering that conventional methods need an explicit model of the system, hence introducing data-driven reachability analysis. Data-driven reachability analysis uses data, inherently produced by an unknown system, to perform future state predictions using sets, generally represented by zonotopes. These predicted sets are generally more conservative than model-based reachable sets. Therefore, is it possible to cluster previously recorded trajectory data based on the expressed behavior and perform the predictions on each cluster to still be able to provide safety guarantees? The theory behind data-driven reachability analysis, which can handle input noise and model uncertainties and still provide safety guarantees, is quite recent. This means that previous implementations for predicting pedestrians are theoretically probabilistic and would not be appropriate to implement in actual systems. Thus, this thesis is not the first of its kind in predicting the future reachable sets for pedestrians using clustered behavioral data, but it is the first work that provides safety guarantees in the process. The method proposed in this thesis first labels the historically recorded trajectories into the behavior also referred to as mode, the pedestrian expressed, which is done by simple conditional statements. This is done offline. However, this implementation is designed to be modular enabling easier improvements to the labelling system. Then, the reachable sets are computed for each behavior separately, which enables a potential motion planner to decide on which modal sets are relevant for specific scenarios. Theoretically, this method provides safety guarantees. The outcomes of this method were more descriptive reachable sets, meaning that the predicted areas intersected areas that it reasonably should, and did not intersect areas it reasonably should not. Also, the volume of the zonotopes for the modal sets was observed to be smaller than the volume of the implemented baseline, indicating fewer over-approximations and less conservative predictions. These results enable more efficient path planning for Connected and Autonomous Vehicles (CAVs), thus reducing fuel consumption and brake wear. / Att predicera framtida tillstånd för fotgängare i urbana situationer är en utmaning, speciellt med tanke på att konventionella metoder behöver uttryckligen en modell av systemet, därav introduceringen av datadriven nåbarhetsanalys. Datadriven nåbarhetsanalys använder data, naturligt producerad av ett okänt system, för att genomföra framtida tillståndspredicering med hjälp av matematiska set, generellt representerade av zonotoper. Dessa predicerade sets är generellt sett mode konservativa än modellbaserade nåbara set. Därmed, är det möjligt att dela upp historiskt inspelade banor baserat på det uttryckta beteendet och genomföra prediceringar på varje kluster och bibehålla säkerhetsgarantier? Teorin bakom datadriven nåbarhetsanalys, som kan hantera brus i indatat och modellosäkerheter och bibehålla säkerhetsgarantier, är väldigt ny. Detta betyder att tidigare implementationer för att predicera fotgängare är, teoretiskt sett, probabilistiska och är inte lämpliga att implementera i riktiga system. Därmed, detta examensarbete är inte det första som predicerar framtida nåbara set för fotgängare genom att använda kluster för beteendedatat, men den är det första arbetet som bibehåller säkerhetsgarantier i processen. Den introducerade metoden i detta examensarbete rubricerar först de tidigare inspelade banorna baserat på beteendet, även kallat läget, som fotgängaren uttrycker, vilket är gjort genom simpla betingade påståenden. Detta görs offline. Dock, denna implementation är designad till att vara modulär vilket underlättar förbättringar till rubriceringssystemet. Fortsättningsvis, beräknas de nåbara seten för varje beteende separat, vilket möjliggör att en potentiell rörelseplanerare kan avgöra vilka beteendeset som är relevanta för specifika scenarion. Teoretiskt sett så ger denna metod säkerhetsgarantier. Resultaten från denna metod var först och främst mer beskrivande nåbara set, vilket betyder att de predicerade områdena korsar områden som de rimligtvis ska korsa, och inte korsar område som de rimligen inte ska korsa. Dessutom, volymen på zonotoperna for beteendeseten observerades att vara mindre än volymen för baslinjeseten, vilket indikerar lägre överskattningar och mindre konservativa prediceringar. Dessa resultat möjliggör mer effektiv rörelseplanering för uppkopplade och autonoma fordon, vilket reducerar bränsleförbrukningen och bromsslitage.

<p dir="ltr">Tracking and characterizing the space debris population in Earth orbit is necessary to ensure that space can continue to be used safely. However, because space objects are affected by non-conservative forces like drag and solar radiation pressure, predicting the long-term evolution of their orbits is impossible without knowledge of their attitude profiles. Such knowledge may be unavailable for inactive satellites or objects of which the observer is not the owner or operator. In many cases, attitude cannot be measured directly because resolved images of space objects are unavailable due to the distance between the object and the observer, and the effects of atmospheric seeing. However, the total brightness of objects can still be measured. A set of brightness measurements over time is referred to as a "light curve.'' An object's observed brightness is influenced by its attitude and other factors such as its orbit, shape, and reflective properties. If some of these other factors are known, attitude information may be extracted from a light curve. Existing methods of solving this attitude inversion problem either require a good initial guess for an object's rotational states or do not provide a full state estimate. The work in this thesis avoids both problems and provides a full state estimate without requiring an initial state guess.</p><p><br></p><p dir="ltr">The attitude estimation process assumes that the observation geometry and the observed object's shape, reflection properties, and inertia tensor are known. In this thesis, an initial method of searching for attitudes that could correspond to each measurement using the viewing sphere is described. These possible attitudes or "pseudo-measurements'' are then used to initialize a probability hypothesis density filter that is theoretically capable of representing the multi-modal nature of the attitude estimate using a Gaussian mixture model. However, the probability hypothesis density filter is found to often diverge from the truth because it is necessary to merge and prune components of the Gaussian mixture model to avoid computational intractability. In its place, a particle swarm optimizer method for performing an attitude inversion has been developed. This method uses analytic attitude solutions to quickly propagate a large number of attitude time histories simultaneously. The particle swarm optimizer method is validated using simulated light curves for several objects. A preliminary attempt is made to estimate the attitude of an object using real light curve measurements.</p>

On the 29th of August, the vessel Stena Scandica experienced a blackout. Before the blackout, 294 alarms were issued in 4 minutes. With the number of alarms, the operators could not prevent the blackout. The amount of information and the way it was presented became a hindrance to operators. They could not interpret their surroundings' information without fault from them. This interpretation is called situational awareness. This thesis will solve how information can be provided to operators without hindrance to situational awareness. The focus will be on the Swedish Navy's operators and their needs. The aim is to solve the problem by creating a system that provides situational awareness. The system will use the information on air- and seaborne targets from a radar and a camera display. Three research questions were proposed: how will the radar data structure be, how will it be ranked, and how will it be presented? The structure was expected to tell the targets' location, size, and movement. The ranking of the targets would tell if the targets were a threat to the naval operators. Lastly, the targets were expected to be presented with some of their information on a camera display.    For the first question, the structure for both kinds of targets was constructed to meet the expectations. Two models were used to solve the second question. An artificial neural network and fuzzy c-means. The artificial neural network was chosen as it is one of the best classification algorithms. Fuzzy c-means were chosen since it can cluster similar behaviors together, therefore clustering high-threat targets together. Of these two models, the result showed that the artificial neural network was a better ranking method, with a higher accuracy of 92.9% for airborne targets and 80.6% for seaborne targets. A simulation was made to answer the third question and was built according to the expectations. The simulation only displayed the highest threat targets in the camera display. By presenting the high-threat targets, the operators received a better understanding of where the targets are in reality. In the future, studies should be conducted on implementation of the system on Swedish Navy vessels. For example, is there enough computational power for an artificial neural network? / Den 29 augusti drabbades fartyget Stena Scandica av ett strömavbrott. Innan strömavbrottet utlöste 294 larm inom 4 minuter, vilket gjorde det omöjligt för operatörerna att förhindra avbrottet. Mängden information och sättet den presenterades på blev ett hinder för operatörerna, vilket påverkade deras lägesbild. Arbete syftar till att lösa hur information kan tillhandahållas till operatörer utan att hindra deras situationsmedvetenhet, med fokus på den svenska marinens operatörer och deras behov. Detta arbete föreslår ett system som använder radardata och kameradisplayer för att tillhandahålla lägesbilden. Tre forskningsfrågor ställs: hur ska radarns datastruktur vara, hur ska den rankas, och hur ska den presenteras? Strukturen förväntas visa målens plats, storlek och rörelse. Rankningen ska indikera om målen utgör ett hot, och hög-hotmål ska presenteras på kameradisplayen. För att svara på den första frågan konstruerades strukturen för båda typerna av mål. För den andra frågan användes två modeller: ett artificiellt neuralt nätverk och fuzzy c-means. Det artificiella neurala nätverket visade sig vara den bästa metoden med en noggrannhet på 92,9% för luftmål och 80,6% för sjömål. En simulering gjordes för att svara på den tredje frågan, där endast de mest hotfulla målen visades på kameradisplayen. Detta gav operatörerna en bättre förståelse för var målen befann sig. Framtida studier bör undersöka systemets implementering på svenska marinens fartyg. Exempelvis om tillräcklig beräkningskraft finns för ett artificiellt neuralt nätverk.

Although most modern, highly-computerized flight decks are known to be robust to small disturbances and failures, humans still play a crucial role in advanced decision making in off-nominal situations, and accidents still occur because of poor human-automation interaction. In addition to the physical state of the environment, operators now have to extend their awareness to the state of the automated flight systems. To guarantee the accuracy of this knowledge, humans need to know the dynamics or approximate versions of the dynamics that rule the automation. The operator's situation awareness can decline because of a deficient mental model of the aircraft and an excessive workload. This work describes the creation of a computational human agent model simulating cognitive constructs such as situation awareness and mental models known to capture the symptoms of poor human-automation interaction and provide insight into more comprehensive metrics supporting the validation of automated systems in aviation.

Den urbana miljön är en av de mest komplexa miljöer som svenska förband ska kunna strida i. Bristfällig information, tredimensionellt hot, korta stridsavstånd och civila är bara några av de utmaningar soldater ställs inför i urban miljö. Vissa av dessa utmaningar skulle kunna minskas genom nyttjande av obemannade sensorer, som skulle kunna ge chefer en tydligare lägesbild. Studien har genomförts som en komparativ fallstudie, där ett representativt flygande system har jämförts med ett markgående. Syftet med studien är att utröna vilket system har störst militär nytta. Utgångspunkt för jämförelsen är en konceptmodell för militär nytta, där systemen jämförts inom tre dimensioner; militär effektivitet, militär lämplighet samt ekonomisk överkomlighet. Utfallet av studien tyder på att det markgående systemet, UGV, har en något högre militär nytta än det flygande systemet. Vidare påvisar studien att det finns tydliga skillnader i kravuppfyllnad beroende på var och i vilken strid som systemen används. Det flygande har en tydlig fördel utanför byggnader under anfallsstrid, medan det markgående har en fördel inne i byggnader. Studien påvisar även att båda systemen bidrar med militär nytta till förband i urban miljö. / The urban environment is one of the most difficult environments that Swedish units are supposed to fight in. Inadequate information, a three-dimensional threat, close range combat and civilians are just a few of the challenges that face soldiers in urban environment. Some of these challenges could be reduced by the use of unmanned sensors, which could enhance the situational awareness. This study has been conducted as a comparative case study, where a representative aerial system has been compared with a ground system. The aim of the study is to determine which system has the highest military utility. The study is based on a concept model for military utility that has compared the systems within three dimensions; military effectiveness, military suitability and affordability. The result of the study indicates that the ground system, UGV, has a slight advantage in military utility compared to the aerial system. Further the study indicates that there are apparent differences between the systems depending on where they are used and in what combat situation. The aerial system has a clear advantage when used outside buildings and in offensive actions, whereas the ground system has an advantage inside buildings. The study also shows that both systems contributes with military utility to units in urban terrain.

Noise pollution is widespread in today’s society, from traffic to industrial sources, and many people are confined to unhealthy acoustic environments through their employment. When noise cannot be eliminated at the sound source, the protective measure is usually to equip the employee with hearing protection devices (HPDs), e.g. earmuffs, that attenuates the noise. Applying excessive attenuation can render the wearer disconnected from her acoustic environment and present risks and social isolation. HPDs using level-dependent function (LDF) uses ambient microphones together with a signal processing unit and internal loudspeakers to achieve attenuation flexibility – if ambient noise is low, the loudspeakers can amplify the sound to combat an excessive passive attenuation of the HPD, and if ambient noise is high this amplification is decreased to maintain healthy sound levels. Appropriate levels of attenuation allows the user to detect ambient sounds which contributes to the their situational awareness. Besides attenuating the sounds however, HPDs does change the spectral content of the sound which may complicate the ability to identify them. This thesis has focused on exploring the human ability to identify sounds, design a method for measuring this ability, implement it and evaluate its result and the method itself. The thesis has been conducted in collaboration with 3MTM PeltorTM and was intended to evaluate the effect of earmuffs with LDF on the ability to identify sounds in a process industry soundscape. The progression of the thesis was structured within the Double Diamond process, where literature reviews, benchmarking and workshops informed the research and resulted in a method design that was analyzed using a repeated measures ANOVA based on empirical data from 16 participants. Surprisingly, the results showed no statistically significant advantage from wearing a HPD using LDF, as opposed to no LDF, in the user accuracy or certainty in sound identification. The reasons for this are discussed with a focus on evaluating the usefulness of the designed method in future efforts to develop HPDs that afford reliable sound identification. / Bullerföroreningar är ett utbrett problem i dagens samhälle, från trafik till industriella källor, och många människor är låsta till en ohälsosam akustisk miljö via sin anställning. När buller inte kan elimineras vid ljudkällan är skyddsåtgärden vanligtvis att förse arbetstagaren med hörselskydd, exempelvis hörselkåpor, som dämpar bullret. Att använda en överdriven dämpning kan dock göra att användaren avskiljs helt från sin akustiska miljö och innebära risker samt social isolering. Hörselskydd utrustade med nivåberoende funktion (LDF) använder mikrofoner tillsammans med en signalbehandlingsenhet och interna högtalare för att uppnå flexibilitet i dämpningen – om omgivande buller är lågt kan högtalarna förstärka ljudet för att motverka en överdriven passiv dämpning erbjuden av hörselskyddet, om omgivande buller däremot är högt kan denna förstärkning minskas för att vidhålla sunda ljudnivåer. Lämpliga dämpningsnivåer gör att användaren kan upptäcka omgivande ljud som bidrar till deras situationsmedvetenhet. Förutom att dämpa ljuden så ändrar dock hörselskydd också det spektrala innehållet i ljudet vilket kan komplicera förmågan att identifiera ljuden. Denna masteruppsats har fokuserat på att utforska människans förmåga att identifiera ljud, utforma en metod för att mäta denna förmåga, implementera metoden och utvärdera dess resultat samt metoden i sig. Uppsatsen har genomförts i samarbete med 3MTM PeltorTM och var avsedd att utvärdera effekten av hörselkåpor, utrustade med LDF, på förmågan att identifiera ljud i ett ljudlandskap likt det i en processindustri. Projektet var strukturerad enligt Double Diamondprocessen, där litteraturstudier, benchmarking och workshops har informerat och väglett arbetet och resulterat i en metod som analyserats med hjälp av variansanalys baserat på empiriska data från 16 deltagare. Överraskande nog så visade resultaten ingen statistiskt signifikant fördel med att bära ett hörselskydd som är utrustat med LDF till skillnad från ett hörselskydd som saknar denna funktion, vad gäller användarens förmåga att korrekt identifiera ljud samt rapporterad säkerhet i svaren. Orsaker till detta diskuteras med fokus på att utvärdera användbarheten av den utformade metoden i framtida ansträngningar för att utveckla hörselskydd som erbjuder tillförlitlig ljudidentifiering.

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Utvecklingen inom fordonsindustrin går mot autonoma fordon, och som effekt av det behöver både säkerhet och samhällets acceptans öka. För att öka säkerheten i autonoma fordon behöver föraren vara situationsmedveten, vilket kan uppnås genom en röstassistent i fordonet. Om säkerheten skulle öka skulle sannolikt även människors tillit och därav samhällets acceptans öka. Studier pekar på att olika egenskaper så som rösttyp hos röstassistenter kan ha en effekt på förares situationsmedvetenhet och tillit. Denna studie ämnar således att undersöka huruvida en feminin eller maskulin röst hos röstassistenter påverkar förares situationsmedvetenhet och upplevda tillit till autonoma fordon. För att ta reda på detta genomfördes en experimentell studie där deltagare befann sig i en bilsimulator av ett autonomt fordon som simulerade körscenarier. Deltagarna var uppdelade i två grupper där varje grupp fick uppleva en typ av röstassistent, antingen med feminin eller maskulin röst. Deltagarna fick uppleva körscenarier där varningssituationer i olika allvarlighetsgrader uppstod, där de fick fatta beslut kring hur de hade hanterat varningssituationen. I samband med körscenarierna utfördes tre olika datainsamlingar bestående av intervju och enkäter. Resultatet av studien påvisar ingen signifikant eller utmärkande skillnad vad gäller upplevd tillit mellan grupper. Resultatet indikerar inte heller någon utmärkande skillnad mellan gruppernas situationsmedvetenhet. Det finns marginell skillnad mellan grupperna, där gruppen som upplevde feminin röst hade både högre upplevd tillit och situationsmedvetenhet. Slutresultatet i denna studie visar att området behöver utforskas vidare för att upptäcka och förstå eventuella effekter av rösttyper hos röstassistenter. / The development in the automotive industry is moving towards autonomous vehicles, and as a consequence the need to increase both safety and societal acceptance is necessary. To increase safety in autonomous vehicles, the driver needs to be situationally aware, which can be achieved through a voice assistant in the vehicle. If safety were to increase, people's trust and therefore society's acceptance would most likely also increase. Studies indicate that different characteristics of voice assistants, such as the type of voice, can have an effect on drivers' situational awareness and trust. Thus, this study aims to investigate whether a feminine or masculine voice in voice assistants affects drivers' situational awareness and perceived trust in autonomous vehicles. An experimental study was conducted in order to investigate this, where participants were situated in a simulator of an autonomous vehicle that simulated driving scenarios. The participants were divided into two groups where each group experienced one of two types of voice assistants, either with a feminine or a masculine voice. The participants had to experience driving scenarios where warning situations of varying degrees of severity arose, where they had to make decisions about how they would have handled the situation. In connection with the driving scenarios, three types of data collections consisting of interviews and questionnaires were carried out. The results of the study show no significant or distinctive difference in terms of perceived trust between groups. The results also don’t indicate any distinctive difference between the groups' situational awareness. There is marginal difference between the groups, with the group experiencing feminine voice having both higher perceived trust and situational awareness. The final result of this study shows that this research area needs to be explored further to discover and understand the possible effects of voice types in voice assistants.