Obstacles faced in planetary surface exploration require innovation in many areas, primarily that of robotics. To be able to study interesting areas that are by current means hard to reach, such as steep slopes, ravines, caves andlava tubes, the surface vehicles of today need to be modified or augmented. Oneaugmentation with such a goal is PHALANX (Projectile Hordes for AdvancedLong-term and Networked eXploration), a prototype system being developed atthe NASA Ames Research Center. PHALANX uses remote deployment of expendablesensor nodes from a lander or rover vehicle. This enables in-situ measurementsin hard-to-reach areas with reduced risk to the rover. The deployed sensornodes are equipped with capabilities to transmit data wirelessly back to therover and to form a network with the rover and other nodes. Knowledge of the location of deployed sensor nodes and the momentary locationof the rover is greatly desired. PHALANX can be of aid in this aspect as well.With the addition of inter-node and rover-to-node range measurements, arange-based network SLAM (Simultaneous Localization and Mapping) system can beimplemented for the rover to use while it is driving within the network. Theresulting SLAM system in PHALANX shares characteristics with others in the SLAM literature, but with some additions that make it unique. One crucial additionis that the rover itself deploys the nodes. Another is the ability for therover to more accurately localize deployed nodes by external sensing, such asby utilizing the rover cameras. In this thesis, the SLAM of PHALANX is studied by means of computer simulation.The simulation software is created using real mission values and valuesresulting from testing of the PHALANX prototype hardware. An overview of issuesthat a SLAM solution has to face as present in the literature is given in thecontext of the PHALANX SLAM system, such as poor connectivity, and highlycollinear placements of nodes. The system performance and sensitivities arethen investigated for the described issues, using predicted typical PHALANXapplication scenarios. The results are presented as errors in estimated positions of the sensor nodesand in the estimated position of the rover. I find that there are relativesensitivities to the investigated parameters, but that in general SLAM inPHALANX is fairly insensitive. This gives mission planners and operatorsgreater flexibility to prioritize other aspects important to the mission athand. The simulation software developed in this thesis work also has thepotential to be expanded on as a tool for mission planners to prepare forspecific mission scenarios using PHALANX.

Vehicles and machinery play a crucial role in our daily lives, contributing to our transportationneeds and supporting various industries. As society strives for sustainability, the advancementof technology and efficient resource allocation become paramount. However, vehicle faultscontinue to pose a significant challenge, leading to accidents and unfortunate consequences.In this thesis, we aim to address this issue by exploring the effectiveness of an ensemble ofdeep learning models for supervised classification. Specifically, we propose to evaluate the performance of 1D-CNN-Bi-LSTM and 1D-CNN-Bi-GRU models. The Bi-LSTM and Bi-GRUmodels incorporate a multi-head attention mechanism to capture intricate patterns in the data.The methodology involves initial feature extraction using 1D-CNN, followed by learning thetemporal dependencies in the time series data using Bi-LSTM and Bi-GRU. These models aretrained and evaluated on a labeled dataset, yielding promising results. The successful completion of this thesis has met the objectives and scope of the research, and it also paves the way forfuture investigations and further research in this domain.

The aim of the thesis was to deepen the knowledge of sustainability aspects in the field of transportation for the ongoing digital development of autonomous and electrical freight trucks toward the year 2030 in the Swedish context. To achieve the above aim, three objectives were formulated. The first objective was to identify critical factors for the envisioned digital and electrical development, the second objective was to create and design three future scenarios for the year 2030, and the final objective was to apply the scenarios to several stakeholders. The critical factors were identified via the analysis of conducted interviews with several stakeholders. Based on these findings, three scenarios were created, which then were evaluated by several focusgroup interviews and ordinary interviews conducted with additional relevant stakeholders. In the evaluation, radar charts were used to clarify the stakeholders’ views and to assess different factors within each and every scenario. The factors that were evaluated with the radar charts were the drivers' situation, cooperation, investments, competitiveness, capacity, and energy consumption. The obtained results show that all three sustainability aspects have a significant impact on the future and envisioned development of the transportation industry. The outcomes of these objectives were then synthesized into relevant aspects and some formulated recommendations focusing on their role and relevance from a sustainability perspective. The list of recommendations is addressed to Trafikverket, providing the authority with future directions for advancing the ongoing development of autonomous and electric freight trucks. To conclude, the results from the thesis can assist stakeholders to increase sustainability and public welfare in the further development of autonomous and electric freight trucks in Sweden.

With increasing demand for autonomous systems and self-driving heavy-dutyvehicles there is an even more increasing demand for safety. In order to achievedesired safety level on the public roads, engineers have to tackle many technicalissues, like decision making, object detection and perception. In order to detect anobject or to have an understanding of its surroundings, autonomous heavy-dutyvehicles are equipped with different types of sensors. These sensors are placed ondifferent parts of the autonomous truck. The fact that some parts of the truckare highly dynamical introduces additional disturbances to the signals comingfrom onboard sensors. One of the most dynamic parts of every truck is its cabin.Moving cabin may induce additional disturbances into data coming from sensorsattached to it. This corrupted data may lead the autonomous trucks to make wrongdecisions. In the worst case, such decisions may be fatal.This thesis uses a data driven modeling approach for creating a mathematicaldescription of cabin movements based on data from onboard sensors. For thatpurpose, tools from system identification field are used. The resulting modelsare aimed to be used for implementation of real-time estimation algorithm forthe cabin dynamics, which in turn can be used for real-time compensation of thedisturbances. / Ee ökad efterfrågan efter autonoma fordon sätter ännu högre krav på säkerhet.Eftersom mäniskors säkerhet alltid prioriteras högst så måste ingenjörer runtom i världen att se till att framtida självkörande lastbilar inte bara är heltautonoma utan framför allt säkra. För att uppnå önskad säkerhetsnivå på deallmänna vägarna måste ingenjörerna ta itu med många tekniska problem, somexempelvis beslutsfattande, objektdetektering och perception. För att upptäckaett föremål eller att förstå sin omgivning är autonoma lastbilar numera utrustademed olika typer av sensorer. Dessa sensorer är monterade på olika delar av denautonoma lastbilen. Det faktum att vissa delar av en lastbil är mycket dynamiskaintroducerar ytterligare störningar i signler som kommer från de sensorer somfinns monterade på fordonet. En av de mest dynamiska delarna av varje lastbilär hytten. Hyttens rörelser kan orsaka ytterligare störningar i data som kommerfrån sensorer som är anslutna till den. Den felaktiga informationen kan ledatill att det autonoma fordonet fattar felaktiga beslut, som i värsta skulle kunnaorsaka dödsfall. Detta examensarbete använder sig av datadriven modelleringför att beskriva det matematiska förhållandet mellan hytt och chassi baserat pådata som kommer från de sensorer som finns monterade på fordonet. För attdetta ändamål skulle kunna uppnås används systemidentifieringsteknik. Bådegrey-box och black-box systemidentifieringsmetod användas och jämföras för attkunna erhålla ett resultat som visar vilken av de två teknikerna är bäst lämpad fördetta ändamål.

The application of Image Processing to Autonomous driving has drawn significant attention in recently. However, the demanding nature of the image processing algorithms conveys a considerable burden to any conventional realtime implementation. On the other hand, the emergence of FPGAs has brought numerous facilities toward fast prototyping and implementation of ASICs so that an image processing algorithm can be designed, tested and synthesized in a relatively short period in comparison to traditional approaches. This thesis investigates the best combination of current algorithms to reach an optimum solution to the problem of lane detection and tracking, while aiming to fit the design to a minimal system. The proposed structure realizes three algorithms, namely Edge Detector, Hough Transform, and Kalman filter. For each module, the theoretical background is investigated and a detailed description of the realization is given followed by an analysis of both achievements and shortages of the design. It is concluded by describing the advantages of implementing this architecture and the use of these kinds of systems. / Tillämpningen av bildbehandling inom autonoma fordon har fått stor uppmärksamhet den senaste tiden. Emellertid förmedlar den krävande karaktären hos bildbehandlingsalgoritmerna en stor belastning på vilken konventionell realtidsimplementering som helst. Å andra sidan har framväxten av FPGAer medfört många möjligheter till snabb prototypering och implementering av ASICar så att en bildbehandlingsalgoritm kan utformas, testas och syntetiseras på relativt kort tid jämfört med traditionella tillvägagångssätt. Denna avhandling undersöker den bästa kombinationen av nuvarande algoritmer för att uppnå en optimal lösning på problemet med spårning och fildetektering, med målet att krympa designen till ett minimalt system. Den föreslagna strukturen realiserar tre algoritmer, nämligen Edge Detector, Hough Transform och Kalman filter. För varje modul undersöks den teoretiska bakgrunden och en detaljerad beskrivning av realiseringen ges följd av en analys av både fördelar och brister i konstruktionen. Avhandlingen avslutas med en beskrivning av fördelarna med att implementera lösningen på det sätt den görs och hur dessa system kan användas.

During the creation of any concrete floor the concrete needs to be grinded down from it's very rough newly poured form to a more usable floor surface. Concrete floor grinding is very special in that the work area is often immensely large while the height difference on the surface is incredibly small, in-fact the the largest local difference of the surface from a peek to a valley during the grinding process is submillimeter and goes down to micrometer scale. Today's methods for measuring concrete surfaces are very few and all output one dimensional profiles of the surface in very time consuming processes which makes them unsuitable for real-time analysis of the surfaces during the grinding process. Because of this, the effectiveness of the work is dependent on the experience and intuition of the operator of the grinding machine as they have to make the decision of when to move on to the next step in the grinding process. Therefore it is desirable to create a better method for concrete surface measurement that can measure big areas in a short period of time. In this project a structured light method using sinusoidal phase shifting is implemented and evaluated with an easily movable setup that can measure the height of a concrete surface over an area. The method works by encoding the surface with a phase using a projector and analysing how the phase encoding warps when imaging it from an angle. By triangulation this can be made into a height map of the measured area. The end results show that the method is promising for this application and can detect the submillimeter differences. However, more suitable hardware and a more reliable calibration procedure are required to move this prototype towards a more practical measuring device.

In recent years, the EU has observed a decrease in the stocks of certain fish species due to unrestricted fishing. To combat the problem, many fisheries are investigating how to automatically estimate the catch size and composition using sensors onboard the vessels. Yet, measuring the size of fish in marine imagery is a difficult task. The images generally suffer from complex conditions caused by cluttered fish, motion blur and dirty sensors. In this thesis, we propose a novel method for automatic measurement of fish size that can enable measuring both visible and occluded fish. We use a Mask R-CNN to segment the visible regions of the fish, and then fill in the shape of the occluded fish using a U-Net. We train the U-Net to perform shape completion in a semi-supervised manner, by simulating occlusions on an open-source fish dataset. Different to previous shape completion work, we teach the U-Net when to fill in the shape and not by including a small portion of fully visible fish in the input training data. Our results show that our proposed method succeeds to fill in the shape of the synthetically occluded fish as well as of some of the cluttered fish in real marine imagery. We achieve an mIoU score of 93.9 % on 1 000 synthetic test images and present qualitative results on real images captured onboard a fishing vessel. The qualitative results show that the U-Net can fill in the shapes of lightly occluded fish, but struggles when the tail fin is hidden and only parts of the fish body is visible. This task is difficult even for a human, and the performance could perhaps be increased by including the fish appearance in the shape completion task. The simulation-to-reality gap could perhaps also be reduced by finetuning the U-Net on some real occlusions, which could increase the performance on the heavy occlusions in the real marine imagery.

As more and more shared autonomous vehicles (SAVs) are introduced in mixed traffic conditions, it calls upon research exploring the relationship between an SAV and its passengers. It is assumed that in the future SAVs will be completely autonomous, with no operator on-board, resulting in the loss of implicit communication between a driver and a passenger with effects on trust. This served as motivation to perform a study investigating the definition of trust from the passenger’s perspective. Initially a state-of-the-art study was conducted to research previous work and identify existing trust frameworks. Three field studies took place on an operational SAV which included interviews and observations with on-board operator and passengers. The aim of which was to understand the trust dynamics between the operator and passenger but more importantly, between the SAV and its passengers. The results revealed dependency on the operator during the commute in deadlock situations. To investigate trust attributes, interviews and observations were conducted with passengers of a regular bus as well as experts from the field of transportation. Situational awareness of the SAV and communication of SAV intention were deemed most important towards building trust with caution towards information overload. Furthermore, three participatory design studies conducted showed a multi-modal approach as the preferred way of communication, with visual and auditory modalities being the most favorable choice. The overall results showed that a communication and feedback channel with an SAV and its passengers is necessary in creating trust in the absence of a driver. Future studies could use the findings from this thesis as the building blocks for creating a communication interface to enhance passengers trust in an SAV. / Fler och fler autonoma fordon (SAV) introduceras i den vanliga trafikmiljön, vilket kräver ökad förståelse för relationen mellan SAV:er och dess passagerare. I framtiden förutsätts SAV:er kunna bli hela autonoma utan varken en förare eller operatör ombord, vilket ändrar förutsättningarna. Det skulle innebära en förlust av den implicita (“tysta”) kommunikationen mellan förare och passagerare, vilket är bakgrunden för denna studie om passagerares förtroende för SAV:er. Studien innehåller fyra delar. Först en översikt av den senaste forskningen om upplevt förtroende, vilket skapar ett forskningsmässigt ramverk. Tre fältstudier gjordes ombord en SAV vilket inkluderade både intervjuer och observation av passagerare och operatörer. Syftet var att förstå dynamiken både mellan operatören och passageraren samt mellan SAV:n och passageraren. Resultaten visar på att fordonet, och därmed passagerarna, fortfarande är beroende av operatören i situationer när fordonet fastnade på grund av problem i trafikmiljön. Den tredje delen av studie handlade om att undersöka olika parametrar för förtroende och genomfördes med hjälp av intervjuer och observationer av passagerare på en vanlig buss samt genom intervjuer med experter från transportbranschen. Det tydligaste resultatet var att SAV:n behövde vara medveten om närmiljön och att visa förståelse och kommunicera sin avsikt var den faktor som var viktigast för att bygga förtroende. Samtidigt fanns risk för ett överflöd av information. Slutligen genomfördes tre designövningar med användare vilket visade på ett behov av kommunikation med hjälp av flera kanaler, där den visuella och ljudmässiga kanaler föredrogs av de flesta användare. Resultaten tyder på att kommunikation och återkoppling är nödvändigt för att skapa förtroende mellan SAV:er och dess passagerare. Framtida studier bör därför fokusera på att skapa ett gränssnitt mot passagerare som bygger på dessa resultat.

Behavior-driven development (BDD) is a development practice focusing on behaviors and requirements from users and stakeholders. It is designed to develop behaviors which contribute directly to system outcomes. BDD encourages multiple stakeholders to collaborate by minimizing communication gaps and create a shared understanding of the project between technical and non-technical speakers. As a result, the development process becomes faster and the cost lower. Although BDD has many benefits, there are teams who feel inertia towards using it as their main development practice.This thesis work took place at a company with a strong agile foundation. It had the goal to investigate reasons why teams feel inertia against BDD, and thus contribute to BDD research and assist the company. The assumption that positive motivation would help in dispelling inertia was the idea behind this thesis work, and that a stronger motivation for a practice can be achieved by assessing a team’s suitability for it. To reach the goal of the thesis a qualitative research methodology was used, with a focus on obtaining a better understanding of opinions and behaviors that exist, with rounds of interviews and forms as the main method of data collection. Interviews were also consistently used throughout the thesis work to validate that it followed the right track.The inertia which teams at the company have experienced was clustered into different dimensions. These dimensions were used to develop a self-assessment tool intended to help people starting a project to assess how well BDD might fit their context. It allows people to assess their inertia in the different dimensions identified, and as well as attempting to give an overall guide to readiness, also giving some recommendations where gaps could be identified.The deliverable of the thesis work is the tool for managing inertia against BDD. It was developed in a spreadsheet-format for quick development and easy access for multiple users. However, it is important to highlight that this tool focuses on agile autonomous teams. The tool is not about forcing the development practice on someone but rather acts as an aid in giving insight into how well BDD could work for a specific project and team. Finally, to grasp the validity of the tool teams who had previous success developing projects using BDD at the company were able to try it out to see how well it reflected their project reality. The tool also got tested on teams who felt strong inertia towards BDD, to verify whether it helped them manage it or not. / Beteendedriven utveckling (BDD), är ett arbetssätt som fokuserar på beteenden och krav från både användare och intressenter. Det är utvecklat för att främja och skapa beteenen som bidrar till det önskade målet. Några av BDDs fördelar är att arbetssättet uppmuntrar intressenterna till tätt samarbete, att minska luckor i kommunikation och information samt att det skapar en delad förståelse för projektet mellan teknisktoch icke-tekniskt kunniga intressenter. En positiv konsekvens av detta är att utvecklingen tenderar att bli snabbare och kostnaderna lägre. BDD ser till att alla inblandade är eniga om vilket resultat man kan förvänta sig från ett utvecklingsprojekt, redan innan utvecklingen börjar. Detta gör att de missförstånd som är vanligt förekommande mellan intressenter och utvecklingsteam reduceras. Trots att BDD, som nämnts, har flera fördelar finns det team som känner motstånd mot att använda BDD som sitt huvudsakliga arbetssätt.Antagandet att motivation kan bidra till att minska motståndet och att motivationen kan skapas genom att påvisa för team hur lämpligt BDD skulle vara för just dem var ideén bakom detta arbete. Det utfördes på ett företag med stark agil bas. Målet var att identifiera och utreda orsakerna till att team känner motstånd mot BDD, och på så sätt bidra till forskningen och samtidigt hjälpa företaget med en ökad insikt i detta. Syftet med arbetet var att utveckla ett verktyg för att hjälpa team förstå sitt motstånd mot BDD och guida dem till hur de kan hantera det. För att nå målet användes en kvalitativ forskningsmetod med fokus på att få en bättre förståelse för åsikter och beteenden som finns angående BDD. Olika rundor av intervjuer utgjorde den huvudsakliga datainsamlingen. Intervjuer användes också kontinuerligt för att validera att arbetet höll rätt kurs.Det motstånd mot BDD som påträffades på företaget grupperades i olika dimensioner. Dessa användes för att utveckla slutprodukten av kandidatexamensarbetet, ett självskattningsverktyg. För att underlätta utvecklingen valdes ett spreadsheet-format på verktyget, detta även för att enkelt kunna dela det mellan flertalet intressenter.Det är viktigt att understryka att verktyget fokuserar på autonoma team. Det har inte som mål att tvinga någon att använda BDD, utan att agera som hjälp för att visa hur arbetssättet skulle kunna fungera för ett specifikt projekt och team. Slutligen, för att kunna verifiera kvaliteten på verktyget, utvärderades det i samarbete med team som tidigare på ett framgångsrikt sätt utvecklat ett projekt med BDD. Detta för att se om självskattningsverktygets utsägelse motsvarade teamets helhetsupplevelse. Verktyget testades också av andra team för att se om det var till hjälp för dem eller inte.

For safe mobility, an autonomous vehicle must perceive the surroundings accurately. There are many perception tasks associated with understanding the local environment such as object detection, localization, and lane analysis. Object detection, in particular, plays a vital role in determining an object’s location and classifying it correctly and is one of the challenging tasks in the self-driving research area. Before employing an object detection module in autonomous vehicle testing, an organization needs to have a precise analysis of the module. Hence, it becomes crucial for a company to have an evaluation framework to evaluate an object detection algorithm’s performance. This thesis develops a comprehensive framework for evaluating and analyzing object detection algorithms, both 2D (camera images based) and 3D (LiDAR point cloud-based). The pipeline developed in this thesis provides the ability to evaluate multiple models with ease, signified by the key performance metrics, Average Precision, F-score, and Mean Average Precision. 40-point interpolation method is used to calculate the Average Precision. / För säker rörlighet måste ett autonomt fordon uppfatta omgivningen exakt. Det finns många uppfattningsuppgifter associerade med att förstå den lokala miljön, såsom objektdetektering, lokalisering och filanalys. I synnerhet objektdetektering spelar en viktig roll för att bestämma ett objekts plats och klassificera det korrekt och är en av de utmanande uppgifterna inom det självdrivande forskningsområdet. Innan en anställd detekteringsmodul används i autonoma fordonsprovningar måste en organisation ha en exakt analys av modulen. Därför blir det avgörande för ett företag att ha en utvärderingsram för att utvärdera en objektdetekteringsalgoritms prestanda. Denna avhandling utvecklar ett omfattande ramverk för utvärdering och analys av objektdetekteringsalgoritmer, både 2 D (kamerabilder baserade) och 3 D (LiDAR-punktmolnbaserade). Rörledningen som utvecklats i denna avhandling ger möjlighet att enkelt utvärdera flera modeller, betecknad med nyckelprestandamätvärdena, Genomsnittlig precision, F-poäng och genomsnittlig genomsnittlig precision. 40-punkts interpoleringsmetod används för att beräkna medelprecisionen.