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.

Precise and robust automotive localization is a must for autonomous vehicles. Radar is a cheap and robust sensor, and this project aimed to find a method to use automotive radar to localize globally. By using radar data to build occupancy grids based on other state-of-the-art radar localization methods, and applying image correlation techniques, a localization precision of below 20 cm could be achieved, delivering poses at frequency higher than 0.5 Hz along with a characterization of the uncertainty. By using an improved sensor model for the occupancy grid mapping, filtering the radar data, and using image correlation in the Fourier domain. The presented results are better than the state-of-the-art radar localization methods, both in terms of precision and frequency, however not in terms of heading estimation. The work provides a foundation for future investigations and improvements of radar as a sensor for localization. / Exakt och robust fordonslokalisering är ett måste för framtidens autonoma fordon. Radar är billig och robust sensor, och detta projekt utfördes i syfte att hitta en metod för att använda bilradar för att lokalisera globalt. Genom att använda radardata för att bygga occupancyg grids baserade på de senaste bästa radarlokaliseringsmetoder och tillämpa bildkorrelationstekniker, kunde en lokaliseringsprecision bättre än 20 cm uppnås, vilket ger positioner vid frekvens högre än 0,5 Hz tillsammans osäkerhetens karaktärisering. Genom att använda en förbättrad sensormodell för kartläggning av occupancy grids, filtrera radardata och använda bildkorrelation i Fourier- domänen. De presenterade resultaten är bättre än de senaste metoderna för radarlokalisering, både när det gäller precision och frekvens, men inte när det gäller riktning. Arbetet utgör en grund för framtida undersökningar av radar som en sensor för lokalisering.

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Autonomous vehicles are predicted to disrupt the current landscape of urban mobility.Many studies have investigated how autonomous vehicles, either operated as a serviceor as private cars, could compete against public transport and even replace it. Fewerstudies have investigated how autonomous vehicles could actually be an opportunityfor the public transport sector, as a new type of offer that would cover specific needsalong traditional modes such as buses or metros.The aim of this project is to quantify the effect of replacing part of the public transportnetwork of Uppsala by demand-responsive autonomous fleets. This is achieved bybuilding a transport model based on the traditional four-step transport model andcalculating the total cost of the network both from the passenger and the operator’sperspective.The study shows that autonomous vehicles can slightly improve the performance of thenetwork and work best when combined with traditional bus lines. However, they alsoincrease the traffic and have a risk to cause congestion.

Gaffeltruckar är motordrivna fordon som används flitigt inom industrin för att transportera gods. Automatiseringen av dessa är ett steg i linje med digitaliseringen av industrin och ett prioriterat säkerhetsarbete, och då semiautonoma truckar anses säkrare ökar både utbud och efterfrågan på dessa. Syftet med denna rapport var att undersöka hur företag involverar användare samt integrerar säkerhetsaspekter i sin produktutvecklingsprocess av semiautonoma gaffeltruckar för att minimera risken för personskador. Även synen på automatisering bland användare och tillverkare studerades, samt vilka möjligheter och utmaningar som automatiseringen medför. Forskningsfrågorna besvarades med hjälp av en litteraturstudie och en intervjustudie med fem respondenter från tre olika företag. Resultaten från intervjustudien sammanställdes och jämfördes med den teoretiska referensramen i en efterföljande analys och diskussion. Studien visade att säkerhetsaspekterna beaktas och integreras systematiskt i början av produktutvecklingsprocessen. Tillverkarna såväl som användarna ansåg att säkerhet är en allt viktigare aspekt som upplevs ha blivit mycket bättre under bara de senaste tio åren och säkerhetsaspekterna har utvecklats till att bli mer av hygienfaktorer som måste uppnås på grund av lagkrav och regleringar. En upptäckt som gjordes var därför att användare i regel inte involveras i syfte att integrera säkerhetsfunktioner utan snarare för att möta behov som är svårare att definiera och som handlar om hur produkten ska kännas och upplevas vid användning; såsom körkänsla och ergonomi. Det finns goda möjligheter att öka säkerheten med hjälp av automatisering av gaffeltruckar. Många tillbud och olyckor uppstår på grund av mänskliga faktorer, såsom missförstånd och ouppmärksamhet. Att reducera eller eliminera den mänskliga delen skulle troligen bidra till färre problematiska situationer och misstag, vilket kan rädda liv. Andra möjligheter är kopplade till produktivitet, där det bland annat finns ekonomiska vinster att hämta när industrin effektiviseras. Samtidigt finns det alltid utmaningar i samband med stora digitala omställningar. Automatiseringen av materialhanteringen innebär en investering som behöver godkännas och genomföras, vilket även kräver förarbete och efterforskning. Just bristande kunskap var något som framhävdes som ett hinder, men varken bland användare eller tillverkare ansågs hindrena större än möjligheterna. / Forklifts are motor-driven vehicles which are frequently used for the transportation of goods in the industry. The automation of these is a step in the right direction when it comes to digitalization and prioritized safety work, and as semi-autonomous forklifts are regarded as safer, both the supply and the demand for them are increasing. The aim of this report was to investigate how companies involve users and integrate safety aspects in their product development process of semi-autonomous forklifts to minimize the risk for bodily injuries. Furthermore, the view on automation among users and manufacturers was studied, along with the possibilities and challenges automation entails. The research questions were answered through a literature study and an interview study with five respondents from three different companies. The results from the interview study were compiled and compared with the theoretical source in a subsequent analysis and discussion. The study showed that safety aspects are regarded and integrated systematically during the start of the product development process. The manufacturers as well as the users believed that safety is an aspect which is becoming more and more important and which is perceived as having improved immensely during the last ten years. The safety aspects have evolved into more of hygiene factors which need to be reached due to legislative demands and regulations. A finding from this study was therefore that users were not involved with the purpose to integrate safety functions but rather to meet demands which are more difficult to define and which concern how the product should feel and be perceived in usage, such as driving sense and ergonomics. There are good possibilities to increase safety by automation. Many mishaps and accidents originate from human factors, such as misunderstandings and lack of attention. Reducing or eliminating the human contribution would probably lead to fewer problematic situations and mistakes, which could save lives. Other possibilities are in productivity, where there are financial profits to gain when streamlining the industry. However, there are always challenges related to large, digital transitions. Automation of the material handling means an investment which needs to be approved and realized, which also requires preparatory work and research. Lack of knowledge was a suggested obstacle, but neither the users nor the manufacturers considered the obstacles larger than the possibilities.

A major challenge in the transportation industry is how to reduce the emissions of greenhouse gases. One way of achieving this in vehicles is to drive more fuel-efficiently. One recently developed technique that has been successful in reducing the fuel consumption is the look-ahead cruise controller, which utilizes future conditions such as road topography. In this this thesis, similar methods are used in order to reduce the fuel consumption of heavy-duty vehicles driving in environments where the required and desired velocity vary. The main focus is on vehicles in urban driving, which must alter their velocity due to, for instance, changing legal speed restrictions and the presence of intersections. The driving missions of such vehicles are here formulated as optimal control problems. In order to restrict the vehicle to drive in a way that does not deviate too much from a normal way of driving, constraints on the velocity are imposed based on statistics from real truck operation. In a first approach, the vehicle model is based on forces and the cost function involves the consumed energy. This problem is solved both offline using Pontryagin's maximum principle and online using a model predictive controller with a quadratic program formulation. Simulations show that 7 % energy can be saved without increasing the trip time nor deviating from a normal way of driving. In a second approach, the vehicle model is extended to include an engine and a gearbox with the objective of minimizing the fuel consumption. A fuel map for the engine and a polynomial function for the gearbox losses are extracted from experimental data and used in the model. This problem is solved using dynamic programming taking into consideration gear changes, coasting with gear and coasting in neutral. Simulations show that by allowing the use of coasting in neutral gear, 13 % fuel can be saved without increasing the trip time or deviating from a normal way of driving. Finally, an implementation of a rule-based controller into an advanced vehicle model in highway driving is performed. The controller identifies sections of downhills where fuel can be saved by coasting in neutral gear. / En stor utmaning för transportsektorn är hur utsläppen av växthusgaser ska minskas. Detta kan åstadkommas i fordon genom att köra bränslesnålare. En nyligen utvecklad teknik som har varit framgångsrik i att minska bränsleförbrukningen är framförhållningsreglering, som använder framtida förhållanden så som vägtopografi. I denna avhandling används liknande metoder för att minska bränsleförbrukningen i tunga fordon som kör i miljöer där önskad och tvingad hastighet varierar. Fokus ligger framförallt på fordon i stadskörning, där hastigheten måste varieras beroende på bland annat hastighetsbegränsningar och korsningar. Denna typ av körning formuleras här som optimala reglerproblem. För att hindra fordonet från att avvika för mycket från ett normalt körbeteende sätts begränsningar på tillåten hastighet baserat på statistik från verklig körning. Problemet angrips först genom att använda en fordonsmodell baserad på krafter och en kriteriefunktion innehållande energiförbrukning. Problemet löses både offline med Pontryagin's maximum princip och online med modellprediktiv reglering baserad på kvadratisk programmering. Simuleringar visar att 7 % energi kan sparas utan att öka körtiden eller avvika från ett normalt körbeteende. Problemet angrips sedan genom att utöka fordonsmodellen till att också innehålla motor och växellåda med målet att minimera bränsleförbrukningen. Specifik bränsleförbrukning och en polynomisk approximation av förlusterna i växellådan är extraherade från experiment och används i simuleringarna. Problemet löses genom dynamisk programmering som tar hänsyn till växling, släpning och frirullning. Simuleringar visar att 13 % bränsle kan sparas utan att öka körtid eller avvika från normalt körbeteende genom att tillåta frirullning. Slutligen görs en implementering av en regelbaserad regulator på en avancerad fordonsmodell för ett fordon i motorvägskörning. Regulatorn identifierar sektioner med nedförsbackar där bränsle kan sparas genom frirulllning. / <p>QC 20171011</p>

Autonomous Vehicles (AVs) should result in tremendous benefits to safe human transportation. Recent reports indicate a global average of 3,287 road crash related fatalities a day with the blame, in most cases, assigned to the human driver. By replacing the main cause, AVs are predicted to significantly reduce road accidents -- some claiming up to a 90% reduction on US roads. However, achieving these numbers is not simple. AVs are expected to assume tasks that human drivers perform both consciously and unconsciously -- in some instances, with Machine Learning. AVs incur new levels of complexity that, if handled incorrectly, can result in failures that cause loss of human life and damage to the environment. Accidents involving SAE Level 2 vehicles have highlighted such failures and demonstrated that AVs have a long way to go. The path towards safe AVs includes system architectures that provide effective failure monitoring, detection and mitigation. These architectures must produce AVs that degrade gracefully and remain sufficiently operational in the presence of failures. We introduce Safe-AV, a fault tolerant safety architecture for AVs that is based on the commonly adopted E-Gas 3 Level Monitoring Concept, the Simplex Architecture and guided by a thorough hazard analysis in the form of Systems-Theoretic Process Analysis (STPA). We commenced the architecture design with a review of some modern AV accidents which helped identify the types of failures AVs can present and acted as a first step to our STPA. The hazard analysis was applied to an initial AV architecture (without safety mechanisms) consisting of components that should be present in a typical AV (based on the literature and our ideas). Our STPA identified the system level accidents, hazards and corresponding loss scenarios that led to well-founded safety requirements which, in turn, evolved the initial architecture into Safe-AV. / Thesis / Master of Applied Science (MASc)

In the realm of autonomous vehicles, ensuring safety through advanced anomaly detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle sensors, capturing complex temporal patterns efficiently. Additionally, we extend our analysis to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving experiences and the evolution of vehicle safety technologies. By integrating Reservoir Computing with temporal-aware data analysis, this thesis contributes to both driver behavior assessment and traffic flow prediction, addressing critical aspects of autonomous vehicle systems. / Master of Science / Our cities are constantly growing, and traffic congestion is a major challenge. This project explores how innovative technology can help us predict traffic patterns and develop smarter management strategies. Inspired by the rigorous safety systems being developed for self-driving cars, we'll delve into the world of machine learning. By combining advanced techniques for identifying unusual traffic patterns with tools that analyze data over time, we'll gain a deeper understanding of traffic flow and driver behavior. We'll utilize data collected by car sensors, such as speed and turning patterns, to not only predict traffic jams but also see how drivers react in different situations. However, our project has a broader scope than just traffic flow. We aim to leverage this framework to understand driver behavior in general, with a particular focus on its implications for self-driving vehicles. Through meticulous data analysis and sophisticated algorithms, we can categorize drivers based on their performance. This valuable information can be used to develop improved methods for detecting risky situations, ultimately leading to safer roads and smoother traffic flow for everyone. To ensure the effectiveness of our approach, we'll rigorously test it using real-world data from GPS data from taxi fleets and nationally recognized traffic datasets. By harnessing the power of machine learning and tools that can adapt to changing data patterns, this project has the potential to revolutionize traffic management in cities. This paves the way for a future with safer roads, less congestion, and a more positive experience for everyone who lives in and travels through our bustling urban centers.