Developing regions are often characterized by large areas that are poorly reachable or explored. The mapping and census of roaming populations in these areas are often difficult and sporadic. A recent spark in the development of small aerial vehicles has made them the perfect tool to efficiently and accurately monitor these areas. This paper presents an approach to aid area surveying through the use of Unmanned Aerial Vehicles. The two main components of this approach are an efficient on-device deep learning object identification component to capture and infer images with acceptable performance (latency andaccuracy), and a dynamic path planning approach, informed by the object identification component. In particular, this thesis illustrates the development of the path planning component, which exploits potential field methods to dynamically adapt the path based on inputs from the vision system. It also describes the integration work that was performed to implement the approach on a prototype platform, with the aim to achieve autonomous flight with onboard computation. The path planning component was developed with the purpose of gaining information about the populations detected by the object identification component, while considering the limited resources of energy and computational power onboard a UAV. The developed algorithm was compared to navigation using a predefined path, where the UAV does not react to the environment. Results from the comparison show that the algorithm provide more information about the objects of interest, with a very small change in flight time. The integration of the object identification and the path planning components on the prototype platform was evaluated in terms of end-to-end latency, power consumption and resource utilization. Results show that the proposed approach is feasible for area surveying in developing regions. Parts of this work has been published in the workshop of DroNet, collocated with MobiSys, with the title Surveying Areas in Developing Regions Through Context Aware Drone Mobility. Thework was carried out in collaboration with Alessandro Montanari, Alice Valentini, Cecilia Mascoloand Amanda Prorok. / Utvecklingsländer är ofta karaktäriserade av vidsträcka områden som är svåråtkomliga och outforskade. Kartläggning och folkräkning av populationen i dessa områden är svåra uppgifter som sker sporadiskt. Nya framsteg i utvecklingen av små, luftburna fordon har gjort dem till perfekta verktyg för att effektivt och noggrant bevaka dessa områden. Den här rapporten presenterar en strategi för att underlätta utforskning av dessa områden med hjälp av drönare. De två huvudkomponenterna i denna strategi är en effektiv maskininlärningskomponent för objektidentifiering med acceptabel prestanda i avseende av latens och noggrannhet, samt en dynamisk navigeringskomponent som informeras av objektidentifieringskomponenten. I synnerhet illustrerar denna avhandling utvecklingen av navigeringskomponenten, som utnyttjar potentialfält för att dynamiskt anpassa vägen baserat på information från objektidentifieringssystemet. Dessutom beskrivs det integrationsarbete som utförts för att implementera strategin på en prototypplattform, med målet att uppnå autonom flygning med all beräkning utförd ombord. Navigeringskomponenten utvecklades i syfte att maximera informationen om de populationer som upptäckts av objektidentifieringskomponenten, med hänsyn till de begränsade resurserna av energi och beräkningskraft ombord på en drönare. Den utvecklade algoritmen jämfördes med navigering med en fördefinierad väg, där drönaren inte reagerar på omgivningen. Resultat från jämförelsen visar att algoritmen ger mer information om objekten av intresse, med en mycket liten förändring i flygtiden. Integreringen av objektidentifieringskomponenten och navigeringskomponenten på prototypplattformen utvärderades med avseende på latens, strömförbrukning och resursutnyttjande. Resultaten visar att den föreslagna strategin är genomförbar för kartläggning och utforskning av utvecklingsregioner. Delar av detta arbete har publicerats i DroNets workshop, samlokaliserad med MobiSys, med titeln Surveying Areas in Developing Regions Through Context Aware Drone Mobility. Arbetet utfördes i samarbete med Alessandro Montanari, Alice Valentini, Cecilia Mascolo och Amanda Prorok.

The global transportation sector is currently shifting towards autonomous vehicles. This shift comes with challenges, such as; identifying obstacles, recognising its surroundings and acting safely based on these perceptions. To accomplish mentioned tasks, the vehicle is equipped with sensors, such as lidars and cameras. A lesser known, yet significant challenge lies in keeping these sensors clean from dirt and debris which tends to accumulate on the lens of the sensors when the vehicle is moving. This report investigates how lidar- and camera sensors can be cleaned more resource-efficient in comparison to the existing sensor cleaning systems on the market. The goal was to recommend a sensor cleaning system for the range of sensors of an autonomous heavy vehicle.The authors of the study developed and tested several cleaning methods which were evaluated among each other and existing systems, while considering a system perspective. The developed cleaning systems showed that enabling a low washer fluid consumption had a negative impact on the system’s scalability, durability, compactness and complexity, in comparison to the existing cleaning systems. When utilising a high-pressured fluid, the study found that a sweeping flat spray is more resource-efficient than a static cone spray, where the latter is being commonly used in conventional sensor cleaning systems. The concepts with a sweeping flat spray resulted in a fluid consumption 4-7 times lower than the best reference cleaning system. In the case of a lidar, when considering a system perspective, it is recommended to use two telescopic flat spray nozzles facing each other and placed in either corner of the lens. It is also recommended that the nozzles are activated one at a time and that fluid I sprayed immediately on activation and kept flowing during the entire stroke to achieve a shaving or ploughing effect on the dirt. This method of cleaning has been observed to be more resource efficient compared to the reference systems. The resource-efficiency of a sweeping flat spray exists for other lens sizes as well, such as cameras and headlamps, however the scaling effects need further investigating. Therefore, additional tests are suggested, such as stress tests to determine the long-term durability of the cleaning system. Additionally, more research is needed to understand the impact of dirt in different environments and how often the sensors need cleaning. This also includes investigating how dirty the sensors can become before losing functionality. / Den globala transportsektorn är på väg att skifta till autonoma fordon. Detta skifte medför flear utmaningar; som att göra fordonet medveten om dess omgivning, identifiera objekt och agera säkert baserat på dessa intryck. För att kunna utföra dessa uppgifter är fordonen utrustade med sensorer, såsom lidar och kameror. En mindre känd utmaning ligger i att hålla dessa sensorer rena från smuts som ansamlas på sensorernas lins när fordonet framförs. Denna rapport undersöker hur lidar- och kamerasensorer kan rengöras mer resurseffektivt i förhållande till befintliga sensorrengöringssystem på marknaden. Målet var att rekommendera ett rengöringssystem för sensorerna som krävs för autonom färd, nämligen lidar och kameror. Studien utvecklade och testade ett flertal rengöringsmetoder som utvärderades bland varandra och befintliga rengöringssystem, medan samtidigt ta hänsyn till ett systemperspektiv. De utvecklade rengöringssystemen visade att en låg vätskeförbrukning påverkade systemet negativt i aspekter som skalbarhet, hållbarhet, kompakthet och komplexitet, i jämförelse med the befintliga rengöringssystemen. Vid användning av högtrycksvatten fastställde studien att en rörlig platt stråle kan vara mer resurseffektiv än en statisk konisk stråle, där den senare är vanlig bland befintliga rengöringssystem. Koncepten med en rörlig platt stråle hade en vätskeförbrukning som var fyra till sju gånger lägre än närmaste referenssystem. Vid hänsyn till ett systemperspektiv resulterade det rekommendera rengöringssystemet i två teleskopiska munstycken placerade i motstående hörnor av linsen. En i taget utvidgar sig munstyckena samtidigt som de sprutar högtrycksvatten på linsen, därav möjliggörs en rörlig platt stråle och en resurseffektiv rengöringscykel. Att rengöra med en rörlig platt stråle anses även resurseffektiv när det gäller andra storlekar på linsen, såsom en kamera- eller strålkastarlins, däremot måste eventuella följder från skalningen undersökas i vidare arbete. Det föreslås även kompletterande tester, såsom stresstester för att kunna avgöra livslängden på systemet. Vidare, efterfrågas ytterligare undersökningar på inflytande av smuts i olika miljöer, samt hur ofta sensorerna behöver rengöras. Detta inkluderar även undersökningar kring hur smutsiga sensorerna kan bli innan de tappar funktionaliteten.

Autonomous vehicles are complex, technologically opaque, and can vary greatly in what perceptual capabilities they are endowed with. Because of this, it is reasonable to expect people to have difficulties in accurately inferring what an autonomous vehicle has and has not seen, and also how they will act, in a traffic situation. To facilitate effective interaction in traffic, autonomous vehicles should therefore be developed with people’s assumptions in mind, and design efforts should be made to communicate the vehicles' relevant perceptual beliefs. For such efforts to be effective however, they need to be grounded in empirical data of what assumptions people make about autonomous vehicles' perceptual capabilities. Using a novel method, the present study aims to contribute to this by investigating how people's understanding of the visual capabilities of autonomous vehicles compare to their understanding of those of human drivers with respect to (Q1) what the vehicle/driver can and cannot see in various traffic situations, (Q2) how certain they are of Q1, and (Q3) the level of agreement in their judgement of Q1. Additionally, we examine whether (Q4) there is a correlation between individual differences in anthropomorphizing and Q1. The results indicate that people generally believe autonomous vehicles and human drivers have the same perceptual capabilities, and that they therefore are subject to similar limitations. The results also indicate that people are equally certain of their beliefs in both cases, strongly agree in both cases, and that individual differences in anthropomorphizing are not associated with these beliefs. Implications for development of autonomous vehicles and future research are discussed.

The continuous dramatic increase in the urban population creates many problems related to speedy mobility or conventional accessiblity options.However, the rapid evolution of autonomous technology in the field of automotive and information technology(IT) has made it possible to implement autonomous vehicles (AVs) for public transport smart transportation, as a concept, is a contemporary buzzword that should lead to sustainable mobility.In recent years, different smart transport initiative serviced globally, which has been supported increasingly by the private and public sectors. Briefly highlight the history and development of autonomous vehicles, and the SAE the 4 phases of AV. This thesis explores the main research question of how these two munciplaities aim to integrate AVs(autonomous vehicles)into their public transport systems? The two case strategies has examined where driverless transportation has practiced in l∅renskogs,Norway and Alaborg, Denmark demonstrated that autnonmy bus passengers are well aligned with the muncipalities to reduce the amount of car usage. Easy access for vulnerable groups(young children, physically or mentally disabled individuals and elderly persons). So, the autonomy public transport (PT) can make hard reachable places more accessible, leading to social inclusion. The thesis is primarily qualitative methodology was essential to apply and relies on the work of previous researcher, technical reports, workshops and the empirical data was collected from involvement of stakeholders in the public and private sectors besides the municipalities need to take a leadership position in defining autonomy transportation based on the real city′s demand and integrate into sustainable smart transportation planning stragtegies.

Perceiving the environment is a crucial aspect of autonomous vehicles. To plan the route, the autonomous vehicle needs to be able to detect objects such as cars and pedestrians. This is possible through 3D object detection. However, labeling this type of data is time-consuming. By utilizing a virtual environment, there is an opportunity to generate data and label it in a quicker manner. This thesis aims to investigate how well three selected state-of-the-art models perform on a synthetic dataset of point cloud data. The results showed that the models attain a higher average precision compared to a dataset from the real world. This is mainly due to the virtual environment’s simplicity in relation to the real world’s detail. The results also suggest that models using different representations of point cloud data have different capabilities of transferring knowledge to the real world. / Att uppfatta miljön är en avgörande aspekt av autonoma fordon. Till planera rutten behöver det autonoma fordonet kunna upptäcka föremål som bilar och fotgängare. Detta är möjligt genom 3D-objektdetektering. Att märka denna typ av data är dock tidskrävande. Genom att använda en virtuell miljö, finns det en möjlighet att generera data och märka dem på ett snabbare sätt sätt. Denna avhandling syftar till att undersöka hur väl tre valda state-of-the-art modeller utför på en syntetiskt dataset av punktmolndata. Resultaten visade att modellerna uppnår en average precision jämfört med ett dataset från den riktiga världen. Detta beror främst på den virtuella miljöns enkelhet i förhållande till den verkliga världens detaljer. Resultaten tyder också på att modeller som använder olika representationer av punktmolnsdata har olika möjligheter att överföra kunskap till den verkliga världen.

In his speech during the 2016 Speculative Design Symposium, held at the University of California, San Diego, Benjamin Bratton1 rightly argued that the job of 21st century design is to undo (much of) the design of the 20th.A number of recent controversial designs and practices in the business and public sphere have suddenly made ethical design (design ethics2) a hot topic in the design community.This master thesis is a highly critical and fairly philosophical examination of the design profession in the context of the current socio-technical landscape. It analyses the convergence between the fields of design, ethics and disruptive technology. Autonomous transportation is taken as an example to illustrate what circumstances (should) drive designers’ social engagement. Hopefully, it also accommodates for a productive reflection on the place of ethics in a broader social context. By utilising speculative and critical design approaches, the thesis aims to stimulate, provoke and ideally maintain a public discourse on the direction of development of technology and modern societies, and inspire designers to be more critical to the vocational portrayal of their profession. / <p><strong>The degree project is carried out at the Department of Science and Technology (ITN) at Faculty of Science and Engineering, Linköping University</strong></p>

The Autonomous Vehicle is about to enter the mass-market. The question is not about when it will happen but in which conditions, under which form or who will be the first car manufacturer to release an efficient and reliable final product.By now, the equation has not been solved, due to the high price of the technologies needed, the lack of solutions to provide a reliable network, and the necessity to change conventions established a long time ago in terms of responsibility of the driver.Depending on who is talking, the Autonomous vehicle is not only an evolution of a previous product, which is able to evaluate and to progressively transform into something different that we could call a self-driven car. This innovation is one step further and is challenging everything that was established until now in terms of objective criteria expected from a car. This is why some are calling this a disruptive innovation, or even a revolution, in the sense that it has the power to totally change the way we are interacting with our everyday transportation system.To enter into the market, this technology, this product, will have to overcome some challenges, on the technological side but also on the psychological side of his future clients.Therefore, this thesis research analyses why this innovation could be the future of the Automotive industry, where it is coming from, what are the challenges it will have to overcome, which will be the impacts, and the different possible scenarios.

Though multirotor unmanned aerial vehicles (UAVs) have become widely used during the past decade, challenges in autonomy have prevented their widespread use when moving vehicles act as their base stations. Emerging use cases, including maritime surveillance, package delivery and convoy support, require UAVs to autonomously operate in this scenario. This thesis presents improved solutions to both the state estimation and control problems that must be solved to enable robust, autonomous landing of multirotor UAVs onto moving vehicles.Current state-of-the-art UAV landing systems depend on the detection of visual fiducial markers placed on the landing target vehicle. However, in challenging conditions, such as poor lighting, occlusion, or extreme motion, these fiducial markers may be undected for significant periods of time. This thesis demonstrates a state estimation algorithm that tracks and estimates the locations of unknown visual features on the target vehicle. Experimental results show that this method significantly improves the estimation of the state of the target vehicle while the fiducial marker is not detected.This thesis also describes an improved control scheme that enables a multirotor UAV to accurately track a time-dependent trajectory. Rooted in Lie theory, this controller computes the optimal control signal based on an error-state formulation of the UAV dynamics. Simulation and hardware experiments of this control scheme show its accuracy and computational efficiency, making it a viable solution for use in a robust landing system.

Markov processes have a long history of being used to model safety critical systems. However, with the development of autonomous vehicles and their increased complexity, Markov processes have been shown to not be sufficiently precise for reliability calculations. Therefore there has been the need to consider a more general stochastic process, namely the Semi-Markov process (SMP). SMPs allow for transitions with general distributions between different states and can be used to precisely model complex systems. This comes at the cost of increased complexity when calculating the reliability of systems. As such, methods to increase the interpretability of the system and allow for appropriate approximations have been considered and researched. In this thesis, a novel classification approach for transitions in SMP has been defined and complemented with different conjectures and properties. A transition is classified as good or bad by comparing the reliability of the original system with the reliability of any perturbed system, for which the studied transition is more likely to occur. Cases are presented to illustrate the use of this classification technique. Multiple suggestions and conjectures for future work are also presented and discussed. / Markovprocesser har länge använts för att modellera säkerhetskritiska system. Med utvecklingen av autonoma fordon och deras ökade komplexitet, har dock markovprocesser visat sig vara otillräckliga exakta för tillförlitlighetsberäkningar. Därför har det funnits ett behov för en mer allmän stokastisk process, nämligen semi-markovprocessen (SMP). SMP tillåter generella fördelningar mellan tillstånd och kan användas för att modellera komplexa system med hög noggrannhet. Detta innebär dock en ökad komplexitet vid beräkningen av systemens tillförlitlighet. Metoder för att öka systemets tolkningsbarhet och möjliggöra lämpliga approximationer har därför övervägts och undersökts. I den här masteruppsatsen har en ny klassificeringsmetod för övergångar i SMP definierats och kompletteras med olika antaganden och egenskaper. En övergång klassificeras som antingen bra eller dålig genom en jämförelse av tillförlitligheten i det ursprungliga systemets och ett ändrat system, där den studerade övergången har högre sannolikhet att inträffa. Fallstudier presenteras för att exemplifiera användningen av denna klassificeringsteknik. Flera förslag och antaganden för framtida arbete presenteras och diskuteras också.

Autonomous Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testing is being conducted before their future mass deployment. One approach is to test ADSs directly on the road, but it is incredibly costly to cover all rare corner cases. Thus, a popular complementary approach is to evaluate an ADS’s performance in a simulator. Such method is called simulation based testing. However, randomly testing ADSs in simulation is still not efficient enough and the testing results might not transfer to the real-world. This dissertation underscores that the cornerstone of efficient simulation testing lies in crafting optimal testing scenarios. We delineate several pivotal properties for these scenarios: they should induce ADS misbehavior, exhibit diversity, manifest realism, and adhere to user specified rules (e.g., following traffic rules). Subsequent to this identification, our research delves into methodologies to enhance one or more of these properties of the generated scenarios. Specifically, we embark on two distinct lines of approach. First, we develop advanced search strategies to unearth diverse scenarios that provoke ADS to misbehave. Second, we harness the potential of deep generative models to produce scenarios that are both realistic and in compliance with user specified rules. Because of the need for efficiently testing end-to-end behaviors of ADSs against complex, real-world corner cases, we propose AutoFuzz, a novel fuzz testing technique, which can leverage widely-used driving simulators’ API grammars to generate complex driving scenarios. In order to find misbehavior-inducing scenarios, which are very rare, we propose a learning based search method to optimize AutoFuzz. In particular, our method trains a neural network to select and mutate scenarios sampled from an evolutionary search method. AutoFuzz shows promises in efficiently identifying traffic violations for the given ADSs under test. Although AutoFuzz is good at finding violations, as a black-box method, it is agnostic of the cause of the violations. In the second project, we focus on finding violations caused by the failure of fusion component, which fuses the inputs of multiple sensors and provides the ADS a more reliable understanding of the surroundings. In particular, we identify that the fusion component of an industry-grade ADAS can fail to trust the more reliable input sensor and thus lead to a collision. We define misbehavior caused by such a failure as "fusion error". In order to efficiently find fusion errors, we propose a fuzzing framework, named FusED, that uses a novel evolutionary-based search method with objective promoting fusion output to deviate from sensor input. We show that FusED can efficiently reveal fusion errors for an industry-grade ADAS. One issue with the generated scenarios by AutoFuzz or FusED (or any other search based methods) is that all the NPC vehicles are controlled by some low-level controllers, whose behaviors are different from human drivers. This poses a difficulty in transferring the found violations into real world. Some recent work tries to address this problem by using deep generative models. However, the scenarios cannot be easily controlled which is not desirable for users to customize the testing scenarios. As both realism and controllability of the generated traffic are desirable, we propose a novel method called Controllable Traffic Generation (CTG) that achieves both properties simultaneously. In order to preserve realism, we propose a conditional, dynamic enforced diffusion model. In order to satisfy controllability, we propose using a kind of "traffic language" called Signal Temporal Logic (STL) to specify what we want in traffic scenarios (e.g., following road rules). We then leverage STL to guide the conditional diffusion model for generating realistic and controllable traffic. Although CTG can generate realistic and controllable traffic, it still requires domain expertise to specify the STL based loss function. Besides, it models traffic participants independently, resulting in sub-optimal agents interaction modeling. In order to address these issues, we developed CTG++ which enables a user to use language to generate realistic traffic scenario. In particular, we proposed to use GPT4 to translate a command in natural language into a loss function in code. We then use the loss function to guide a scene-level diffusion model, which considers all the vehicles jointly, to generate traffic satisfying the command. We have found that CTG++ can generate query (in natural language)-compliant and realistic traffic simulation. In summary, our four projects discussed in this thesis have solved important problems in efficiently testing ADSs and have had significant influence in the advancement of ADS. Besides, the models and empirical studies we performed can be applicable to other testing and behavior generation problems, such as general ML-based software testing, and multi-agent behavior planning and prediction. I hope this thesis can serve as an inspiration to anyone who is interested in the exciting field of ADS testing and development, and contribute to the realization of the full automation of driving.