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Recent decades have witnessed the phenomenal success of model predictive control (MPC) in a wide spectrum of domains, such as process industries, intelligent transportation, automotive applications, power systems, cyber security, and robotics. For constrained dynamic systems subject to uncertainties, robust MPC is attractive due to its capability of effectively dealing with various types of uncertainties while ensuring optimal performance concerning prescribed performance indices. But most robust MPC schemes require prior knowledge on the uncertainty, which may not be satisfied in practical applications. Therefore, it is desired to design robust MPC algorithms that proactively update the uncertainty description based on the history of inputs and measurements, motivating the development of adaptive MPC. This dissertation investigates four problems in robust and adaptive MPC from theoretical and application points of view. New algorithms are developed to address these issues efficiently with theoretical guarantees of closed-loop performance. Chapter 1 provides an overview of robust MPC, adaptive MPC, and self-triggered MPC, where the recent advances in these fields are reviewed. Chapter 2 presents notations and preliminary results that are used in this dissertation. Chapter 3 investigates adaptive MPC for a class of constrained linear systems with unknown model parameters. Based on the recursive least-squares (RLS) technique, we design an online set-membership system identification scheme to estimate unknown parameters. Then a novel integration of the proposed estimator and homothetic tube MPC is developed to improve closed-loop performance and reduce conservatism. In Chapter 4, a self-triggered adaptive MPC method is proposed for constrained discrete-time nonlinear systems subject to parametric uncertainties and additive disturbances. Based on the zonotope-based reachable set computation, a set-membership parameter estimator is developed to refine a set-valued description of the time-varying parametric uncertainty under the self-triggered scheduling. We leverage this estimation scheme to design a novel self-triggered adaptive MPC approach for uncertain nonlinear systems. The resultant adaptive MPC method can reduce the average sampling frequency further while preserving comparable closed-loop performance compared with the periodic adaptive MPC method. Chapter 5 proposes a robust nonlinear MPC scheme for the visual servoing of quadrotors subject to external disturbances. By using the virtual camera approach, an image-based visual servoing (IBVS) system model is established with decoupled image kinematics and quadrotor dynamics. A robust MPC scheme is developed to maintain the visual target stay within the field of view of the camera, where the tightened state constraints are constructed based on the Lipschitz condition to tackle external disturbances. In Chapter 6, an adaptive MPC scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. We develop an RLS-based set-membership based parameter to improve the prediction accuracy. In the proposed adaptive MPC scheme, a robustness constraint is designed to handle parametric and additive uncertainties. The proposed constraint has the offline computed shape and online updated shrinkage rate, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Chapter 7 shows some conclusion remarks and future research directions. / Graduate

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The reliance on fossil fuels needs to be fizzled out from every part of modern society. Construction equipment is one industry in which the transition away from fossil fuels is underway in favor of electric vehicles. The transition to electric vehicles is affecting the entire system of construction sites, including enhanced feasibility and incentive for increased autonomy. This thesis aims at identifying system-level design implications stemming from the electric transition of construction equipment. By using biomimicry and drawing inspiration from nature, the goal is to produce a functioning product that fulfills the requirements created based on the needs of the users. Within this thesis, Design Thinking is used to identify the needs in theconstruction equipment sector which are then used as the input for an iterative process of reverse biomimetics. The resulting concepts are tested and evaluated in several design sprints, the findings from which result in the final solution. Aided by the Design Thinking methodology human-centered approach, the solution is optimized for the users. A total of 25 needs were identified within eight different categories. A total of seven design sprints were conducted with the findings accumulating in a sensor cleaning system for autonomous vehicles inspired by the pendulum-like oscillating motion of mammals shedding precipitation. Biomimicry is identified as a promising tool to aid in the creative process by introducing novel perspectives and approaches to the problem space. The evolving development and use of autonomous constructionequipment results in the need for clear and unhindered sensor systems which the solution developed in this thesis provides. / Användandet av förbränningsmotorer behöver forslas ut från varje del av det moderna samhället. Konstruktionsindustrin är i starten av övergången till elektriska maskiner. Det är en markant övergång som påverkar varje del av byggarbetsplatsen, i synnerhet motivet till att använda autonoma maskiner som lösning på den mindre energidensitet eldrivna anläggningsmaskiner innebär. Den här uppsatsen undersöker vilka design implikationer som berörs från den elektriska övergången på ett systemperspektiv kring använd- nignen av anläggningsmaskiner, och hur de ska lösas. Som stöd för att lösa de implikationerna används biomikry. Genom att använda biomikry sökes den lösningen som bäst uppfyller de design implika- tionerna som uppstått och behoven som utforskatts. Inom den här uppsattsen används Design Thinking for att identifiera de behoven som uppstått inom industrin. De fynden blir basen för an- vändandet av en specifik del av biomimikry, nämnligen motsatt biomi- tik. De koncepten som utvecklas testas och evalueras i flera så kallade Design sprints, där fynden från flera Design sprints utformar slut- lösningen. Med stöd från människofokuset av Design Thinking blir slutlösningen direkt optimerad för användaren. Totalt 25 behov identifierades inom åtta kategorier. Totalt sju Design sprints utfördes där de induviduella resultaten utformade en slutlös- ning i form av ett sensortvättsystem för autonoma fordon, vilket tagit inspiration från den svängande rörelsen som används av däggdjur för att få bort vatten. Lösningen innebär ohindrad funcktunalitet av sen- sorerna på autonoma anläggningsmaskiner oberoende av väder eller klimat. Biomimikry har identifierats som ett lovande verktyg för att öka den kreativa processen genom att introducera nytänkande perspektiv där problemet adresseras på ett annorlunda sätt. Det utökade behovet för autonoma anläggningsmaskiner har resulterat i ett ohindrade sensor rengöringssystem vilket är specifikt det den här uppsatsen behandlar.

Modern Advanced Driver Assistant Systems (ADAS) focus more on daytime driving and primarily use daylight cameras as the main vision sources to detect, classify, and track objects. However, evidence has proved that autonomous driving using such a setup is compromised in the dark, and consequently, resulting in accidents. The hypothesis is that adding an infrared camera to the existing ADAS will boost the detection rate and accuracy, and further enhance the overall safety. This thesis investigates how well a standalone infrared camera performs onboard vehicle perception tasks such as object detection and classification using both machine learning and deep learning algorithms. Given a custom labeled infrared driving dataset that contains 4 classes of objects, “People”, “Vehicle”, “Bicycle”, and “Animal”, multiple attempts and improvements of training a supervised learning model, namely the linear multi-class Support Vector Machine (SVM) has been made by using various image preprocessing and feature extraction methods to detect the objects. During training, hard example mining is used to reduce the number of false classifications. This SVM employs a One-Against-All (OAA) styled approach and uses the image pyramid technique to enable multi-scale detection. On the deep learning side, a Convolutional Neural Network (CNN) based state-of-the-art detector, the YOLOv4 family including the full-sized and tiny YOLOv4 has been selected, trained, and tested at different input sizes using the same dataset. Labeling format conversion is performed to make this work. The results show that using bilateral filtering with the Histogram of Oriented Gradients (HOG) feature to train an SVM is preferable and is more accurate than the YOLOv4 family. However, the YOLOv4 networks are significantly faster. Overall, a standalone infrared camera cannot provide dominant detection results, but it can definitely supply useful information to the ADAS and complement other sensory devices for improved safety. / Thesis / Master of Applied Science (MASc)

To fully utilize the abilities of current autonomous vehicles, it is necessary to understand the interactions between vehicles and their operators. Since the current state of the art of autonomous vehicles is partial autonomy that requires operators to perform parts of the driving task and be alert and ready to take over full control of the vehicle, it is necessary to know how operators' abilities are impacted by the amount of autonomy present in the system. Autonomous systems have known effects on performance, cognitive load, and situation awareness, but little is known about how these effects change in relation to distinct, increasing autonomy levels. It is also necessary to consider these abilities with the addition of secondary tasks due to the appeal of using autonomous systems for multitasking. The goal of this research is to use a web-based virtual reality study to model operator situation awareness, cognitive load, driving performance, and secondary task performance as a function of five distinct, increasing levels of partial vehicle autonomy first with a constant, low rate of secondary tasks and then with an increasing rate of secondary tasks. The study had each participant operate a virtual military vehicle in one of five possible autonomy conditions while responding to questions on a communications terminal. After a practice phase for familiarization, participants took part in two drives where they would have to intervene to prevent crashes regardless of autonomy level. The first drive had a slow, steady rate of communication questions, and the second increased the rate of questions to an unmanageable point before slowing down again. For both phases, the factors of scored driving performance, secondary task performance (accuracy and latency), subjective situation awareness from the Situation Awareness Rating Technique (SART), objective situation awareness from real-time probes, and cognitive load from the NASA Task Load Index (NASA-TLX) and the SOS Scale were analyzed in terms of how they related to the autonomy level and to each other. Results are presented in the form of statistical analysis and modeled equations and show the potential for optimal multitasking within specific autonomy levels and task allocation requirements.

<div>Unmanned aerial vehicles (UAVs) have gained more attention in recent years because of their ability to execute various missions. However, recent works have identified vulnerabilities in UAV systems that make them more readily prone to cyberattacks. In this work, the vulnerabilities in the communication channel between the UAV and ground control station are exploited to implement cyberattacks, specifically, the denial of service and false data injection attacks. Unlike other related studies that implemented attacks in simulations, we demonstrate the actual implementation of these attacks on a Holybro S500 quadrotor with PX4 autopilot firmware and MAVLink communication protocol.</div><div><br></div><div>The goal was to create a lightweight intrusion detection system (IDS) that leverages recurrent neural networks (RNNs) to accurately detect cyberattacks, even when implemented on a resource-constrained platform. Different types of RNNs, including simple RNNs, long short-term memory, gated recurrent units, and simple recurrent units, were trained and tested on actual experimental data. A recursive feature elimination approach was carried out on selected features to remove redundant features and to create a lighter RNN IDS model. We also studied the resource consumption of these RNNs on an Arduino Uno board, the lowest-cost companion computer that can be implemented with PX4 autopilot firmware and Pixhawk autopilot boards. The results show that a simple RNN has the best accuracy while also satisfying the constraints of the selected computer.<br></div>

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<p>  </p> <p>The last century has witnessed increased urban sprawl, motorization, and the attendant problems of congestion, safety, and emissions associated with current-day transportation systems. Contemporary literature suggests that emerging transportation technologies, including vehicle autonomy and connectivity, offer great promise in addressing these adversities. As such, highway agencies seek guidance on infrastructure preparations for connected and automated vehicle (CAV) operations. A key area of such preparations is the management of lanes to serve CAVs and human-driven vehicles (HDVs), including the deployment of dedicated lanes for CAVs. There is a need to address the demand and supply perspectives of CAV preparations. On the demand side, agencies need to model the trends and uncertainties of CAV market penetration and level of autonomy during the CAV transition period. On the supply side, agencies need to schedule the CAV-related roadway infrastructure in a way that progressively addresses the growing demand. </p> <p>In addressing these research questions, this dissertation first carries out an economics-based lane allocation for CAVs and HDVs in a highway corridor by determining the optimum number of CAVLs by minimizing road user cost. Next, the dissertation carries out such allocation considering the environment (community emissions cost). Third, the dissertation addresses elements of social and economic sustainability using a CAV-enabled tradable credit scheme that minimizes user travel time subject to social equity constraints. Further, this dissertation provides guidance on how CAV-dedicated lanes, in conjunction with market-based tradable travel credits, could enable the road agency to achieve maximum efficiency of the existing road infrastructure in the CAV transition period. The study framework can serve as a valuable decision-support tool for road agencies in their long-term planning and budgeting in anticipation of the CAV transition period. The key outcome of the framework is an optimal schedule for deploying CAV-dedicated lanes over a given analysis period of several decades in a manner commensurate with CAV demand projections and sustainability-related objectives and constraints.</p>

Autonomous driving systems are rapidly improving and may have the ability to change society in the coming decade. One important part of these systems is the interpretation of sensor information into trajectories of objects. In this master’s thesis, we study an energy minimisation method with radar and camera measurements as inputs. An energy is associated with the trajectories; this takes the measurements, the objects’ dynamics and more factors into consideration. The trajectories are chosen to minimise this energy, using a gradient descent method. The lower the energy, the better the trajectories are expected to match the real world. The processing is performed offline, as opposed to in real time. Offline tracking can be used in the evaluation of the sensors’ and the real time tracker’s performance. Offline processing allows for the use of more computer power. It also gives the possibility to use data that was collected after the considered point in time. A study of the parameters of the used energy minimisation method is presented, along with variations of the initial method. The results of the method is an improvement over the individual inputs, as well as over the real time processing used in the cars currently. In the parameter study it is shown which components of the energy function are improving the results. / Mycket resurser läggs på utveckling av självkörande bilsystem. Dessa kan komma att förändra samhället under det kommande decenniet. En viktig del av dessa system är behandling och tolkning av sensordata och skapande av banor för objekt i omgivningen. I detta examensarbete studeras en energiminimeringsmetod tillsammans med radar- och kameramätningar. En energi beräknas för banorna. Denna tar mätningarna, objektets dynamik och fler faktorer i beaktande. Banorna väljs för att minimera denna energi med hjälp av gradientmetoden. Ju lägre energi, desto bättre förväntas banorna att matcha verkligheten. Bearbetning sker offline i motsats till i realtid; offline-bearbetning kan användas då prestandan för sensorer och realtidsbehandlingen utvärderas. Detta möjliggör användning av mer datorkraft och ger möjlighet att använda data som samlats in efter den aktuella tidpunkten. En studie av de ingående parametrarna i den använda energiminimeringsmetoden presenteras, tillsammans med justeringar av den ursprungliga metoden. Metoden ger ett förbättrat resultat jämfört med de enskilda sensormätningarna, och även jämfört med den realtidsmetod som används i bilarna för närvarande. I parameterstudien visas vilka komponenter i energifunktionen som förbättrar metodens prestanda.