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A novel paper using Canada's real traffic accident data to propose a cost-prediction-based decision-making framework incorporating different ethical foundations for AVs. / Autonomous Vehicles (AVs) hold out the promise of being safer than manually driven cars. However, it is impossible to guarantee the hundred percent avoidance of collisions in a real-life environment with unpredictable objects and events. When accidents become unavoidable, the different reactions of AVs and their outcome will have different consequences. Thus, AVs should incorporate the so-called ‘ethical decision-making algorithm’ when facing unavoidable collisions. This paper is introducing a novel cost-prediction-based decision-making framework incorporating two common ethical foundations human drivers use when facing unavoidable dilemma inducing collisions: Ethical Egoism and Utilitarianism. The cost-prediction algorithm consists of Collision Injury Severity Level Prediction (CISLP) and Cost Evaluation. The CISLP model was trained using both Multinominal Logistic Regression (MLR) and a Decision Tree Classifier (DTC). Both algorithms consider the combination of relationships among traffic collision explanatory features. Four different Cost Evaluation metrics were purposed and compared to suit different application needs. The data set used for training and testing the cost prediction algorithm is the 1999-2017 National Collision Data Base (NCDB) which ensures the realistic and reliability of the algorithm. This paper is a novel paper using Canada's real traffic accident data to propose a cost-prediction-based decision-making framework incorporating different ethical foundations for AVs. / Thesis / Master of Applied Science (MASc)

Autonomous vehicles are developing at a rapid pace while pedestrians' experience with autonomous vehicles is less researched. This paper reported an exploratory study where 40 participants encountered a conditionally autonomous vehicle with unusual driver behaviors at crossing by watching videos and photos. Questionnaires and semi-structured interviews were used to investigate pedestrians' experience. The results showed distracted driver behaviors in the conditionally autonomous driving context had negative impact on pedestrians' crossing experience. Black window on conditionally autonomous vehicles made pedestrians feel uncomfortable and worried. / Autonoma fordon utvecklas i snabb takt medan fotgängares erfarenhet av autonoma fordon är mindre undersökt. I denna uppsats redovisades en undersökande studie där 40 deltagare observerat ett villkorligt autonomt fordon med ovanliga förarbeteenden vid en korsning, genom att titta på videor och foton. Frågeformulär och semi-strukturerade intervjuer användes för att undersöka fotgängares erfarenhet. Resultaten visade att distraherade förarbeteenden i det villkorliga autonoma förhållandet hade negativ inverkan på fotgängares upplevelse vid övergångsstället. Svarta vindrutor på villkorligt autonoma fordon gör att fotgängare känner sig obekväma och oroliga.

Monocular cameras are prominently used for estimating motion of Unmanned Aerial Vehicles. With growing interest in autonomous vehicle technology, the use of monocular cameras in ground vehicles is on the rise. This is especially favorable for localization in situations where Global Navigation Satellite System (GNSS) is unreliable, such as open-pit mining environments. However, most monocular camera based approaches suffer due to obscure scale information. Ground vehicles impose a greater difficulty due to high speeds and fast movements. This thesis aims to estimate the scale of monocular vision data by using an inertial sensor in addition to the camera. It is shown that the simultaneous estimation of pose and scale in autonomous ground vehicles is possible by the fusion of visual and inertial sensors in an Extended Kalman Filter (EKF) framework. However, the convergence of scale is sensitive to several factors including the initialization error. An accurate estimation of scale allows the accurate estimation of pose. This facilitates the localization of ground vehicles in the absence of GNSS, providing a reliable fall-back option. / Monokulära kameror används ofta vid rörelseestimering av obemannade flygande farkoster. Med det ökade intresset för autonoma fordon har även användningen av monokulära kameror i fordon ökat. Detta är fram för allt fördelaktigt i situationer där satellitnavigering (Global Navigation Satellite System (GNSS)) äropålitlig, exempelvis i dagbrott. De flesta system som använder sig av monokulära kameror har problem med att estimera skalan. Denna estimering blir ännu svårare på grund av ett fordons större hastigheter och snabbare rörelser. Syftet med detta exjobb är att försöka estimera skalan baserat på bild data från en monokulär kamera, genom att komplettera med data från tröghetssensorer. Det visas att simultan estimering av position och skala för ett fordon är möjligt genom fusion av bild- och tröghetsdata från sensorer med hjälp av ett utökat Kalmanfilter (EKF). Estimeringens konvergens beror på flera faktorer, inklusive initialiseringsfel. En noggrann estimering av skalan möjliggör också en noggrann estimering av positionen. Detta möjliggör lokalisering av fordon vid avsaknad av GNSS och erbjuder därmed en ökad redundans.

The purpose of this report is to improve the flow of trafficin intersections through the use of a dynamic algorithm.People spend on average more than six weeks commuting towork in Stockholm every year. A large part of the time thatis spent in traffic is due to the time delay in intersections.In this report, sensors that measure speed and distanceto the vehicle are used instead of detectors that only knowif a car is there or not. There are existing solutions that canoptimise the flow for autonomous cars but since the trafficmay consist of a mix of autonomous and human drivenvehicles during the upcoming 40 years those solutions arenot usable for some time.In this work, a system that can handle both autonomousand human driven vehicles is created. The limitation of thesystem is that it can only receive two cars coming from differentdirections simultaneously. The system does not workfor car queues. The system measures the speed of- and thedistance to the vehicles continuously.According to the simulations that were made the algorithmthat has been designed through this project is moretime efficient than the system that is in place today, assumingthat the assumptions that were made for the currentsystem are correct.The conclusion in this report is that it is possible tomake a system that is more time efficient than the one thatis in use today. / Syftet med den här rapporten är att förbättra flödet ikorsningar genom en dynamisk algoritm. Människor sitterdrygt 6 veckor i bilköer varje år. En stor del av av denspenderande tiden i traffiken är på grund av att fordonbehöver stanna i korsningar.I den här rapporten har sensorer som mäter hastighetoch distans använts istället för dagens detektorer som endastkänner av om ett fordon kör över detektorn eller inte.Det finns andra rapporter med lösningar för att öka flödeti korsningar för självkörande bilar men om man kollar pådet kommande 40 åren kommer det troligtsvis att vara enblandning av självkörande och mänskligt körda bilar.I det här arbetet skapas ett system som kan interageramed både mänskligt körda och autonoma bilar. Begränsningarnai det här systemet är att systemet endast kan taemot två bilar som kommer från olika ingångar i korsningensamtidigt. Systemet fungerar inte för bilköer. Systemet mäterden nuvarande hastigheten och distansen på fordonen.Systemet fungerar för alla olika kombinationer av mänskligtoch självkörande bilar.Resultatet av den här rapporten är att en algoritm harutvecklats och är mer tidseffektivt än systemet som användsi Sverige idag, med våra antaganden om systemet som harutveklats i den här rapporten och systemet som användsidag. Resultatet är baserat på korsningar där bara två bilarmöts utan köer.Slutsatsen av den här rapporten är att det är möjligtatt göra ett system som är mer tidseffektivt än systemetvi använder oss av idag, men vi kan inte säkertsätlla attsystemet i den här rapporten är mer robus och driftsäkertän det som används i Sverige idag.

In recent years, the rapid development of autonomous vehicles (AVs) has necessitated the need for high-quality perception systems. Perception is a fundamental requirement for AVs, with cameras and LiDARs being commonly used sensors for environmental understanding and localization. However, there is a research gap in assessing the quality of AVs perception systems. To address this gap, this dissertation proposes a novel paradigm for evaluating AVs perception quality by studying the perception quality of cameras and LiDARs sensors. Our proposed paradigm aims to provide a comprehensive assessment of the quality of perception systems used in AVs.To achieve our research goals, we first validate the concept of surrounding environmental complexity through subjective experiments that rate complexity scores. In this study, we propose a neural network to classify complexity. Subsequently, we study image-based perception quality assessment by using image saliency and 2D object detection algorithms to create an image-based quality index. We then develop a neural network model to regress the proposed quality index score. Furthermore, we extend our research to LiDAR-based point cloud quality assessment by using the image-based saliency map as guidance to generate a point cloud quality index score. We then develop a neural network model to regress the score. Finally, we validate the proposed perception quality index with a novel designed AVs perception algorithm. In conclusion, this dissertation makes a significant contribution to the field of AVs perception by proposing a new paradigm for assessing perception quality. Our research findings can be used to improve the overall performance and safety of AVs, which has significant implications for the transportation industry and society as a whole. / Doctor of Philosophy / This dissertation delves into the fundamentals of autonomous vehicles (AVs), which is perception, with the aim of developing a new paradigm for evaluating the quality of perception algorithms. AVs are the dream of humanity, and perception is the fundamental requirement for achieving their full potential. Our research proposes a new approach to assessing the quality of perception algorithms, which can have significant implications for the performance and safety of AVs. By studying the perception algorithm quality, we aim to identify areas for improvement, leading to better AV performance and enhancing user trust. Our findings highlight the importance of perception in the development of AVs and demonstrate the need for continuous evaluation and improvement of the perception algorithms used in AVs.

This project originated from the question of “whatis the intersection between architecture andmobility in the future urban landscape? ”Mobility and architecture are two seperate fieldscontinously evolving in parallel, yet they exist ina very symbiotic relationship with one another. Mobility influences urban planning and urbanplanning influences mobility. So to understand the future of mobility, it isessential that we try to understand the future ofthe context mobility will reside in. Globally our cities are growing, the density keepsincreasing and people’s living space is becomingsmaller. To sustain the ongoing growth citieswill have to expand by focusing on dynamicarchitecture, such as pre-fabricated homes thatare smaller and quicker to set up. The aim of this project is to rethink what mobilityand especially the car as we know today can provideto us besides transportation. In recent years newtechnologies and developments have proved thatfuture of the car will go beyond explusively beinga means of transport, but also a mobile space fornew experiences and use cases. The image of thecar as we know could be redrawn.This project takes its foundation in the futureurban landscape of Copenhagen. Although afictional setting, the context is build on researchand masterplans for Copenhagen and other citiescarried out by several architecture firms. Amongthem noticeably JAJA Architects’ masterplanwhich reimagines the old “five-finger-plan” forCopenhagen where a more democratic and slowertraffic structure is implemented. Copenhagen-based studio SPACE10’s “Spaces on Wheels”and Toyota and BIG’s “Woven City” also servedas a benchmark in regards to how vehicles willbecome moving spaces as an extentions of thecity architecture.The process for the development of the projectincluded research into emerging technologieswithin mobility, a look into the future urban setting,and the complexities of living there as a youngfamily. Site visits and talks with young parents gaveinsights into the home space and the experiencesthat goes on in there.The design exploration was carried out throughanalogue and digital sketching, technicalpackaging and feasibility studies as well asscenario-mapping and brainstorm sessions. The project was strongly inspired by NIO’sdesign language and principles of Pure, Human,Progressive and Sophisticated. Also Scandinaviandesign played an important role in the designfunctionality and aesthetic. The result is NIO Homi, a fully autonomous smallfootprint car that works as an extension to thehome by providing the family with a space forwork, play and relaxation as well as a means oftransportation.

<p dir="ltr">Introduces a new framework and state-of-the-art algorithm in closed-loop prediction for motion planning under differential constraints. More specifically, this work introduces the idea of sampling on specific "sampling regions" rather than the entire workspace to speed-up the motion planning process by orders of magnitude.</p>

This dissertation addresses path planning for an autonomous vehicle navigating in a two dimensional environment for which an a priori map is inaccurate and for which the environment is sensed in real-time. For this class of application, planning decisions must be made in real-time. This work is motivated by the need for fast autonomous vehicles that require planning algorithms to operate as quickly as possible. In this dissertation, we first study the case in which there are only static obstacles in the environment. We propose a hybrid receding horizon control path planning algorithm that is based on level-set methods. The hybrid method uses global or local level sets in the formulation of the receding horizon control problem. The decision to select a new level set is made based on certain matching conditions that guarantee the optimality of the path. We rigorously prove sufficient conditions that guarantee that the vehicle will converge to the goal as long as a path to the goal exists. We then extend the proposed receding horizon formulation to the case when the environment possesses moving obstacles. Since all of the results in this dissertation are based on level-set methods, we rigorously investigate how level sets change in response to new information locally sensed by a vehicle. The result is a dynamic fast marching algorithm that usually requires significantly less computation that would otherwise be the case. We demonstrate the proposed dynamic fast marching method in a successful field trial for which an autonomous surface vehicle navigated four kilometers through a riverine environment. / Ph. D.