This thesis summarizes an approach for building a trajectory-tracking framework for autonomous robots working in low-speed and controlled space. A modularized robot framework can provide easy access to hardware and software replacement, which can be a tool for validating trajectory-tracking algorithms in controlled laboratory conditions. An introduction to other existing methods for trajectory tracking is presented. These advanced trajectory control methods and studies aim to improve trajectory tracking control for better performance under different environments. This research uses ROS as the middleware for connecting the actuators and computing units. A market-existing global position measurement tool, the UWB system, was selected as the primary localization sensor. A Raspberry Pi and an Arduino Uno are used for high-level and low-level control. The separation of the control units benefits the modularization design of the framework. A robust control approach has also been introduced to prevent the disturbance of uneven terrain to improve the framework's capability to drive arbitrary robot chassis in different testing grounds. During each stage of development, there are offline and online tests for live control tests. The trajectory tracking controller requires a robot kinematic model and tracking control program for better results of controlled behaviour. A custom trajectory control program was made and implemented into the tests. A digital simulation and a physical robot are built to validate the algorithm and the designed framework for performance validation. This framework aims to suit the other scholar's developments and can be used as a testing platform to implement their autonomous driving algorithms or additional sensors. By replacing the control algorithm in the existing trajectory-tracking robotic framework, this autonomous, universal platform may benefit the validation of these algorithms' performance in the field experiment. / Thesis / Master of Applied Science (MASc) / This thesis contains five chapters. Chapter 1 provided the information and background for this research topic regarding the key components, methods, and tools for creating trajectory tracking. Chapter 2 focuses on the existing methods and deep study of tools, equipment and hardware setups for trajectory tracking in simulation and physical setups. The experiments referenced from other studies can benefit the research and development work for the current trajectory tracking development work. The review provides different kinematics models for robot layouts, which impacts the final design of the field experiment robot. Chapter 3 presents the design work process for creating a controller based on the final field experiment robot. This chapter provides steps and considerations while building the control system for a trajectory-tracking robot from scratch. Chapter 4 demonstrates the simulation results and field experiment results. A study of error analysis and repeatability justification can also be found in this chapter. Chapter 5 summarizes the research and development contribution, primary findings, and concerns for identified problems.

Highly automated driving systems promise increased road traffic safety, as well as positive impacts on sustainable transportation by means of increased traffic efficiency and environmental friendliness. The design and development of such systems require scientific advances in a number of areas. One area is the vehicle's electrical/electronic (E/E) architecture. The E/E architecture can be presented using a number of views, of which an important one is the functional view. The functional view describes the decomposition of the system into its main logical components, along with the hierarchical structure, the component inter-connections, and requirements. When this view captures the principal ideas and patterns that constitute the foundation of a variety of specific architectures, it may be termed as a reference architecture. Two reference architectures for highly automated driving form the principal contribution of this thesis. The first reference architecture is for cooperative driving. In a cooperative driving situation, vehicles and road infrastructure in the vicinity of a vehicle continuously exchange wireless information and this information is then used to control the motion of the vehicle. The second reference architecture is for autonomous driving, wherein the vehicle is capable of driver-less operation even without direct communication with external entities. The description of both reference architectures includes their main components and the rationale for how these components should be distributed across the architecture and its layers. These architectures have been validated via multiple real-world instantiations, and the guidelines for instantiation also form part of the architecture description. A comparison with similar architectures is also provided, in order to highlight the similarities and differences. The comparisons show that in the context of automated driving, the explicit recognition of components for semantic understanding, world modeling, and vehicle platform abstraction are unique to the proposed architecture. These components are not unusual in architectures within the Artificial Intelligence/robotics domains; the proposed architecture shows how they can be applied within the automotive domain. A secondary contribution of this thesis is a description of a lightweight, four step approach for model based systems engineering of highly automated driving systems, along with supporting model classes. The model classes cover the concept of operations, logical architecture, application software components, and the implementation platforms. The thesis also provides an overview of current implementation technologies for cognitive driving intelligence and vehicle platform control, and recommends a specific setup for development and accelerated testing of highly automated driving systems, that includes model- and hardware-in-the-loop techniques in conjunction with a publish/subscribe bus. Beyond the more "traditional" engineering concepts, the thesis also investigates the domain of machine consciousness and computational self-awareness. The exploration indicates that current engineering methods are likely to hit a complexity ceiling, breaking through which may require advances in how safety-critical systems can self-organize, construct, and evaluate internal models to reflect their perception of the world. Finally, the thesis also presents a functional architecture for the brake system of an autonomous truck. This architecture proposes a reconfiguration of the existing brake systems of the truck in a way that provides dynamic, diversified redundancy, and an increase in the system reliability and availability, while meeting safety requirements. / <p>QC 20151216</p>

Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of availability of an accessible demonstration platform. In this work, we propose utilizing the F1Tenth platform as an evaluation task for neuromorphic computing. F1Tenth is a competition wherein one tenth scale cars compete in an autonomous racing task; there are significant open source resources in both software and hardware for realizing this task. We present a workflow with neuromorphic hardware, software, and training that can be used to develop a spiking neural network for neuromorphic hardware deployment to perform autonomous racing. We present initial results on utilizing this approach for this small-scale, real-world autonomous vehicle task.

Perceiving the surrounding multiple vehicles robustly and effectively is a very important step in building Advanced Driving Assistant System (ADAS) or autonomous vehicles. This thesis presents the design of the Light Detection and Ranging (LiDAR) perception system which consists of several sub-tasks: ground detection, object detection, object classification, and object tracking. It is believed that accomplishing these sub-tasks will provide a guideline to a vast range of potential autonomous vehicles applications. More specifically, a probability occupancy grid map based approach was developed for ground detection to address the issues of over-segmentation, under-segmentation and slow-segmentation by non-flat surface. Given the non-ground points, point cloud clustering algorithm is developed for object detection by using a Radially Bounded Nearest Neighbor (RBNN) method on the static Kd-tree. To identify the object, a supervised learning approach based on our LiDAR sensor for vehicle type classification is proposed. The proposed classification algorithm is used to classify the object into four different types: ``Sedan'', ``SUV'', ``Van'', and ``Truck''. To handle disturbances and motion uncertainties, a generalized form of Smooth Variable Structure Filter (SVSF) integrated with a combination of Hungarian algorithm (HA) and Probability Data Association Filter (PDAF), referred to as GSVSF-HA/PDAF, is developed. The developed approach is to overcome the multiple targets data association in the content of dynamics environment where the distribution of data is unpredictable. Last but not the least, a comprehensive experimental evaluation for each sub-task is presented to validate the robustness and effectiveness of our developed perception system. / Thesis / Doctor of Philosophy (PhD)

Effective multi-object tracking is crucial for autonomous vehicles to navigate safely and efficiently in dynamic environments. To make autonomous vehicles more affordable one area to address is the computational limitations of the sensors, therefore, cameras are often the first choice sensor. Three challenges in implementation of multi-object tracking in autonomous vehicles are: 1) In these vehicles, sensors like cameras are not static, which can cause motion blur in the frames and make tracking inefficient. 2) Traditional methods for motion compensation, such as those used in Kalman Filter-based Multi-Object Tracking, require extensive parameter tuning to match features between consecutive frames accurately. 3) Simple intersection over union (IoU) metric is insufficient for reliable identification in such environments. This thesis proposes a novel methodology for 2D multi-object tracking in autonomous vehicles using a camera-based Tracking-by-Detection (TBD) approach, emphasizing four key innovations: (1) A real-time deblurring module to mitigate motion blur, ensuring clearer frames for accurate detection; (2) deep learning-based motion compensation module that adapts dynamically to varying motion patterns, enhancing robustness; (3) adaptive cost function for association, incorporating object appearance and temporal consistency to improve upon traditional IoU metrics; (4) The integration of the Unscented Kalman Filter to effectively address non-linearities in the tracking process, enhancing state estimation accuracy. To maintain a Simple Online and Realtime (SORT) framework, we enhance detection by fine-tuning YOLOv8 and YOLOv9 models using autonomous driving datasets like BDD100K and KITTI, which are specifically tailored for these scenarios. Additionally, we incorporate a non-linear approach using the UKF to better capture the influence of various tracking dynamics, further improving tracking performance. Our evaluations show that the proposed methodology significantly outperforms existing state-of-the-art methods while maintaining the same inference rate as the baseline SORT model. These advancements not only improve the accuracy and reliability of multi-object tracking but also reduce the computational burden associated with parameter tuning and motion compensation. Consequently, this work presents a robust and efficient tracking solution for autonomous vehicles, making it viable for real-world deployment under both computational and cost constraints. / Master of Science / Tracking multiple objects is really important for self-driving cars to move safely in busy places. Cameras are often the best choice because they are cheaper and easier to use, but using cameras comes with three main challenges: (1) When cars move, cameras can make blurry images, which makes it harder to see and track things; (2) Traditional tracking methods, like Kalman Filters, need a lot of adjustments to work well; (3) Simple methods, like checking if objects overlap (called Intersection over Union), are not always good enough in crowded, complicated places. This thesis presents a new way to track lots of things using cameras, with four big improvements: (1) A real-time deblurring system that fixes blurry pictures so the camera can see things more clearly; (2) A smart system that uses deep learning to follow movement better; (3) A better way to match objects by using not just their positions but also how they look and move over time, which is better than old IoU methods; (4) A special tool called the Unscented Kalman Filter that helps track objects more accurately when their movements aren't simple or straight. To keep everything simple, fast, and real-time, we use object detectors to help find objects, and we train them with special self-driving datasets like BDD100K and KITTI. These datasets are great for showing the kinds of situations self-driving cars deal with. The Unscented Kalman Filter helps us track objects with more complicated movements, making everything more accurate. Our study show that this new way works much better than older methods, without making the system slower. These improvements make tracking more reliable and cut down on the time needed for tuning and adjusting. Overall, this work provides a strong and simple solution for tracking things in self-driving cars, even if the computer isn't super powerful or the budget is small.

It is inevitable that as a person ages they will encounter different physical and cognitive impairments as well as dynamic social issues. We started this project under the assumption that autonomous driving would greatly benefit the fastest growing population in developed countries, the elderly. However, the larger question at hand was how are older drivers going to interact with future automated driving systems? It was through the qualitative research we conducted that we were able to uncover the answer to this question; older drivers are not willing to give up “control” to autonomous cars. As interaction designers, we need to define what type of interactions need to occur in these future automated driving systems, so older drivers still feel independent and in control when driving. Lawrence D. Burns, former Vice president of Research and Development at General Motors and author of Reinventing the Automobile Personal Urban Mobility for the 21st Century talks about two driving factors that will shape the future of the automobile. These factors are energy and connectivity (Burns et al., 2010). We would add a third one, which is control. If we address these three factors we might be able to bridge the gap between how we drive today and how we will drive in the future and thus create more cohesive future automated driving systems.

Autonomous Driving or self driving vehicles are concepts of vehicles knowing their environment and making driving manoeuvres without instructions from a driver. The concepts have been around for decades but has improved significantly in the last years since research in this area has made significant progress. Benefits of autonomous driving include the possibility to decrease the number of accidents in traffic and thereby saving lives. A major challenge in autonomous driving is to acquire 3D information and relations between all objects in surrounding traffic. This is referred to as \textit{spatial perception}. Stereo camera systems have become a central sensor module for advanced driver assistance systems and autonomous driving. For object detection and measurements at large distances stereo vision encounter difficulties. This includes objects being small, having low contrast and the presence of image noise. Having an accurate perception of the environment at large distances is however of high interest for many applications, especially autonomous driving. This thesis proposes a method which tries to increase the range to where generic objects are first detected using a given stereo camera setup. Objects are represented by planes in 3D space. The input image is segmented into the various objects and the 3D plane parameters are estimated jointly. The 3D plane parameters are estimated directly from the stereo image pairs. In particular, this thesis investigates methods to introduce geometric constraints to the segmentation or labeling task, i.e assigning each considered pixel in the image to a plane. The methods provided in this thesis show that despite the difficulties at large distances it is possible to exploit planar primitives in 3D space for obstacle detection at distances where other methods fail. / En autonom bil innebär att bilen har en uppfattning om sin omgivning och kan utifran det ta beslut angående hur bilen ska manövreras. Konceptet med självkörande bilar har existerat i årtionden men har utvecklats snabbt senaste åren sedan billigare datorkraft finns lättare tillgänglig. Fördelar med autonomiska bilar innebär bland annat att antalet olyckor i trafiken minskas och därmed liv räddas. En av de största utmaningarna med autonoma bilar är att få 3D information och relationer mellan objekt som finns i den omgivande trafikmiljön. Detta kallas för spatial perception och innebär att detektera alla objekt och tilldela en korrekt postition till dem. Stereo kamerasystem har fått en central roll för avancerade förarsystem och autonoma bilar. För detektion av objekt på stora avstånd träffar stereo system på svårigheter. Detta inkluderar väldigt små objekt, låg kontrast och närvaron av brus i bilden. Att ha en ackurativ perception på stora avstånd är dock vitalt för många applikationer, inte minst autonoma bilar. Den här rapporten föreslar en metod som försöker öka avståndet till där objekt först upptäcks. Objekt representeras av plan i 3D rymden. Bilder givna från stereo par segmenteras i olika object och plan parametrar estimeras samtidigt. Planens parametrar estimeras direkt från stereo bild paren. Den här rapporten utreder metoder att introducera gemoetriska begränsningar att använda vid segmenteringsuppgiften. Metoderna som presenteras i denna rapport visar att trots den höga närvaron av brus på stora avstånd är det möjligt att estimera geometriska objekt som är starka nog att möjliggöra detektion av objekt på ett avstand där andra metoder misslyckas.

Alloy is a lightweight formal modeling tool that generates instances of a software specification to check properties of the design. This work demonstrates the use of Alloy for the rapid development of autonomous vehicle driving protocols. We contribute two driving protocols: a Normal protocol that represents the unpredictable yet safe driving behavior of typical human drivers, and a Connected protocol that employs connected technology for cooperative autonomous driving. Using five properties that define safe and productive driving actions, we analyze the performance of our protocols in mixed traffic. Lightweight formal modeling is a valuable way to reason about driving protocols early in the development process because it can automate the checking of safety and productivity properties and prevent costly design flaws.

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Trusting autonomous vehicles is seen as crucial for their dissemination. However, research on autonomous driving so far is restricted by using closed training courses or simulators and by comparing behaviour and evaluation while driving oneself (a manual car) with being driven (by an autonomous car). In the current study, we investigated passengers’ eye movements, categorized as safety-relevant or not safety-relevant, and trust ratings while being driven, once manually and once by an autonomous car, in real traffic as well as in a simulator. As some of the effects observed in the field experiment might have been caused by driving style, driving style was additionally varied in the simulator. Fixations in safety-relevant regions (e.g., on the road and steering wheel) were observed more frequently during safety critical driving situations than during regular driving. More safety-relevant fixations for the autonomous compared to the manual driving mode were observed particularly in the field. Trust ratings were affected by driving mode mainly in the simulator: Here, being driven autonomously led to a lower reported trust than believing to be driven by a human driver. Driving style showed to affect trust ratings, but not gaze behaviour in the simulator experiment. Correlations between gazing into safety relevant regions and trust ratings were of smaller descriptive size than in recent investigations on drivers, suggesting that gazing into safety-relevant regions as objective alternative to trust ratings may not be as exhaustive for passengers as for drivers.