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<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>

These days most of the research related to autonomous vehicle technology focuses on vehicle technology itself and lesser on road infrastructure, including geometric design. This research project aims to lower the deficiency of research works required to make the optimized geometric road design for autonomous vehicle sustainable. In geometric design, significant concerns are designing the road geometrics such as lane width, the radius of horizontal curves, sag vertical curves and crest vertical curves, extra widening, setback distance, and intersection, making the road safer for the vehicles to travel comfortably.Road geometrics is widely designed using the stopping sight distance model, which provides sufficient time to avoid accidents and is efficient. Here in the research work, the stopping sight design model is used for autonomous vehicle technology. At first, the art of autonomous vehicle technology is studied, and a significant difference between autonomous vehicle technology and human-driven vehicle to apply stopping sight distance model is figured out. A literature study is also done for the geometric design of the road for the vehicle with the human driver and autonomous vehicle. The AASHTO model derived for the human-driven vehicle is used and modified for the autonomous vehicle, which gives the optimized geometric design for the autonomous vehicle. The Optimized geometric design parameter is designed individually in AutoCAD Civil 3D. Two road designs follow this in a random rural topography consisting of a normal road design for the vehicle with the human driver and a fully autonomous vehicle. Finally, the sustainability of optimized geometric design compared to road design for the human-driven vehicle is checked in terms of earthwork, pavement surface areas, and pavement materials volume. The result shows that the optimization of a geometric road design for autonomous vehicles is sustainable and extensive research is required.

<p dir="ltr">The emergence of Autonomous Mobile Robots (AMR) signifies a pivotal shift in vehicle-based material handling systems, demonstrating their effectiveness across a broad spectrum of applications. Advancing beyond the traditional Automated Guided Vehicles (AGV), AMRs offer unprecedented flexibility in movement, liberated from electromagnetic guidance constraints. Their decentralized control architecture not only enables remarkable scalability but also fortifies system resilience through advanced conflict resolution mechanisms. Nevertheless, transitioning from AGV to AMR presents intricate challenges, chiefly due to the expanded complexity in path planning and task selection, compounded by the heightened potential for conflicts from their dynamic interaction capabilities. This dissertation confronts these challenges by fully leveraging the technological advancements of AMRs. A kinematic-enabled agent-based simulator was developed to replicate AMR system behavior, enabling detailed analysis of fleet dynamics and interactions within AMR intralogistics systems and their environments. Additionally, a comprehensive fleet management protocol was formulated to enhance the throughput of AMR-based intralogistics systems from an integrated perspective. A pivotal discovery of this research is the inadequacy of existing path planning protocols to provide reliable plans throughout their execution, leading to task allocation decisions based on inaccurate plan information and resulting in false optimality. In response, a novel machine learning enhanced probabilistic Multi-Robot Path Planning (MRPP) protocol was introduced to ensure the generation of dependable path plans, laying a solid foundation for task allocation decisions. The contributions of this dissertation, including the kinematic-enabled simulator, the fleet management protocol, and the MRPP protocol, are intended to pave the way for practical enhancements in autonomous vehicle-based material handling systems, fostering the development of solutions that are both innovative and applicable in industrial practices.<br></p>

Developments of commercial self-driving vehicle (SDV) technology has a potential for a paradigm shift in traffic control technology. Contrary to some previous research approaches, this research argues that, as any other technology, traffic control technology for SDVs should be developed having in mind improved quality of life through a sustainable developmental approach. Consequently, this research emphasizes upon the social perspective of sustainability, considering its neglect in the conventional control principles, and the importance of behavioral considerations for accurately predicting impacts upon economic or environmental factors. The premise is that traffic control technology can affect the distribution of advantages and disadvantages in a society, and thus it requires a framework of social justice. The framework of social justice is inspired by John Rawls' Theory of Justice as fairness, and tries to protect the inviolability of each user in a system. Consequently, the control objective is the distribution of delay per individual, considering for example that the effect of delay is not the same if a person is traveling to a grocery store as opposed to traveling to a hospital. The notion of social justice is developed as a priority system, with end-user responsibility, where user is able to assign a specific Priority Level for each individual trip with SDV. Selected Priority Level is used to determine the right-of-way for each self-driving vehicle at an intersection. As a supporting mechanism to the priority system, there is a structure of non-monetary Priority Credits. Rules for using Priority Credits are determined using knowledge from social science research and through empirical evaluation using surveys, interviews, and web-based experiment. In the physical space, the intersection control principle is developed as hierarchical self-organization, utilizing communication, sensing, and in-vehicle technological capabilities. This distributed control approach should enable robustness against failure, and scalability for future expansion. The control mechanism has been modeled as an agent-based system, allowing evaluation of effects upon safety and user delay. In conclusion, by reaching across multiple disciplines, this development provides the promise and the challenge for evolving SDV control technology. Future efforts for SDV technology development should continue to rely upon transparent public involvement and understanding of human decision-making. / Ph. D.

<p dir="ltr">Unmanned surface vehicles (USVs) offer a versatile platform for various maritime applications, including research, surveillance, and search-and-rescue operations. A critical capability for USVs is maintaining position (station keeping) in dynamic environments and coordinating movement with other USVs (formation control) for collaborative missions. This thesis investigates control strategies for USVs operating in challenging conditions. </p><p dir="ltr">The initial focus is on evaluating traditional control methods like Backstepping and Sliding Mode controllers for station keeping in simulated environments with disturbances. The results from these tests pointed towards the need for a more robust control technique, like deep-learning based control for enhanced performance. </p><p dir="ltr">The thesis then explores formation control, a crucial aspect of cooperative USV missions. A vision-based passive control strategy utilizing a virtual leader concept is proposed. This approach leverages onboard cameras to detect markers on other USVs, eliminating the need for direct communication and potentially improving scalability and resilience. </p><p dir="ltr">Then the thesis presents vision-based formation control architecture and the station keeping controller evaluations. Simulation results are presented, analyzed, and used to draw conclusions about the effectiveness of the proposed approaches. Finally, the thesis discusses the implications of the findings and proposes potential future research directions</p>

In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, Driver Assistance Systems (DAS) such as cruise control, Anti-Lock Braking Systems (ABS), and Electronic Stability Control (ESC) have become widely popular in the automotive industry. Therefore, vehicle control research attracts attention from both academia and industry, and has been an active area of vehicle research for over 30 years, resulting in impressive DAS contributions. Although current vehicle control systems have improved vehicle safety and performance, there is room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming local driving environment such as terrain roughness, elevation grade, bank angle, curvature, and friction. The local driving environment is measured in advance with a terrain measurement system to provide terrain data. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing the response measurements of a preceding vehicle. The response measurements of a preceding vehicle are acquired through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I) communication. The identification method analyzes the response measurements of a preceding vehicle to estimate road data. The estimated road data or the pre-measured road data is used as the upcoming driving environment information for the developed vehicle control system. The metric that objectively quantifies vehicle performance, the Performance Margin, is developed to accomplish the control objectives in an efficient manner. The metric is used as a control reference input and continuously estimated to predict current and future vehicle performance. Next, the predictive control algorithm is developed based on the upcoming driving environment and the performance metric. The developed system predicts future vehicle dynamics states using the upcoming driving environment and the Performance Margin. If the algorithm detects the risks of future vehicle dynamics, the control system intervenes between the driver's input commands based on estimated future vehicle states. The developed control system maintains vehicle handling capabilities based on the results of the prediction by regulating the metric into an acceptable range. By these processes, the developed control system ensures that the vehicle maintains stability consistently, and improves vehicle performance for the near future even if there are undesirable and unexpected driving circumstances. To implement and evaluate the integrated systems of this work, the real-time driving simulator, which uses precise real-world driving environment data, has been developed for advanced high computational vehicle control systems. The developed vehicle control system is implemented in the driving simulator, and the results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS. / Ph. D. / In the field of modern automotive engineering, many researchers are focusing on the development of advanced vehicle control systems such as autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS). Furthermore, cruise control, Anti-Lock Braking Systems, and Electronic Stability Controls have become widely popular in the automotive industry. Although vehicle control systems have improved vehicle safety and performance, there is still room for improvement for dealing with various situations. The objective of the research is to develop a predictive vehicle control system for improving vehicle safety and performance for autonomous vehicles and ADAS. In order to improve the vehicle control system, the proposed system utilizes information about the upcoming driving conditions such as road roughness, elevation grade, bank angle, and curvature. The driving environment is measured in advance with a terrain measurement system. Furthermore, in order to obtain the information about road conditions that cannot be measured in advance, this work begins by analyzing a preceding vehicle’s response to the road. The combined road data is used as the upcoming driving environment information. The measurement that indicates vehicle performance, the Performance Margin, is developed to accomplish the research objectives. It is used in the developed control system, which predicts future vehicle performance. If the system detects future risks, the control system will intervene to correct the driver’s input commands. By these processes, the developed system ensures that the vehicle maintains stability, and improves vehicle performance regardless of the upcoming and unexpected driving conditions. To implement and evaluate the proposed systems, a driving simulator has been developed. The results show that the proposed system is a clear improvement on autonomous vehicle systems and ADAS.

Volvo Construction Equipment (VCE) manufactures wheel loaders, articulated haulers, and excavators. By the end of 2030, the company hopes to have reduced the carbon footprint of its machines by 30 %. To increase energy efficiency and productivity, VCE is focused on developing futuristic wheel loaders that are both electric and autonomous. VCE has unveiled its latest autonomous wheel loader prototype called Zeux. This thesis work aims to create a simulation setup that includes a vehicle model of Zeux and a driver model that is optimised for the machine to complete a certain drive/load cycle. This simulation setup will be used to examine the machine’s performance, energy usage, and efficiency and compare it to a conventional machine to determine its advantages and limitations. The new vehicle model was created by modifying a conventional electric machine’s vehicle modeland a new four-wheel steering system was developed. A driver model was developed based on a condition-based decision tree and state machines with unique controllers for each driver input. This complete vehicle-driver simulation set-up has been tunedand optimised with respect to energy efficiency and productivity. The simulation results are then compared to the results of a similar conventional electric machine simulation model. According to the comparison study, the autonomous wheel loader concept has better productivity, lower hydraulic energy consumption as well as lower overall energy consumption compared to the conventional machine. It can complete the drive cycle much more efficiently despite having a similar powertrain and loading unit as the conventional machine. / Volvo Construction Equipment (VCE) tillverkar hjullastare, midjestyrda dumprar och grävmaskiner. I slutet av 2030 hoppas företaget ha minskat koldioxidavtrycket för sina maskiner med 30 %. För att öka energieffektiviteten och produktivitet är VCE fokuserade på att utveckla framtida hjullastare som både är elektriska och autonoma. VCE har presenterat sitt senaste autonoma hjullastarprototyp som heter Zeux. Detta examensarbete syftar till att skapa en simuleringsmiljö som innehåller en fordonsmodell av Zeux och en förarmodell som är optimerad för att maskinen ska klara en viss kör-/lastcykel. De framtagna modellerna ska sedan användas för att undersöka maskinens prestanda, energianvändning och effektivitet och jämföra resultaten med en konventionell elektrisk maskin för att fastställa dess fördelar och begränsningar. Den nya fordonsmodellen skapades genom att modifiera en konventionell elektrisk maskins fordonsmodell och ett nytt fyrhjulsstyrningssystem utvecklades. En förarmodell utvecklades baserad på ett tillståndsbaserat beslutsträd och tillståndsmaskiner med unika regulatorer för varje drivrutin. Den kompletta simuleringsmodellen har justerats och optimerats med avseende på energianvändning och produktivitet. Resultaten jämfördes sedan med simuleringsresultat av en liknande konventionell elektrisk hjullastare. Enligt jämförelsestudien, har konceptet med autonoma hjullastare bättre produktivitet, lägre hydrauliskenergiförbrukning samt lägre total energiförbrukning jämfört med den konventionella maskinen. Den kan slutföra körcykeln mycket mer effektivt samtidigt trots att den har en liknande drivlina och lastenhet som den konventionell maskin.

Ce travail porte sur la conception d’un système capable d’effectuer des manœuvres de parking automatique plus polyvalent que ceux actuellement commercialisés, tout en conservant une définition technique des capteurs extéroceptifs limités en prix et en gabarit. Un cas d’usage typique est de permettre au véhicule de se rendre automatiquement dans la zone de garage du domicile de son propriétaire, cette fonction est classiquement appelée voiturier autonome à domicile. Partant de l’existant et connaissant les performances attendues, une architecture système et une architecture fonctionnelle ont été tracées. Cela a permis de constituer un ensemble de fonctions interconnectées qui ont participé dans la création d’une architecture software modulaire ainsi que dans la création des interfaces de connexion au véhicule prototype. Dans un premier temps, nous explorons la problématique de la détection d’obstacles. Partant d’un système propriétaire fermé de capteurs ultrason, nous avons réussi à réaliser une carte d’obstacle à un niveau de précision supérieur au produit d’origine. Une augmentation de la limite de détection des capteurs ultrason a été réalisée utilisant une technique Structure from Motion. Ces informations d’occupation ont été exploitées par la suite pour traiter la problématique de détection du couloir de navigation. Dans un second temps, la fonction de localisation du véhicule est abordée. Trois techniques de localisation collaborent pour une robustesse de fonctionnement continu : la localisation odométrique, la localisation par appariement des grilles d’occupation et la localisation par appariement entre une image actuelle et une base d’images adaptée à notre besoin et améliorée en termes de temps de calcul. Enfin, nous nous sommes intéressés à la problématique de navigation du véhicule. Nous avons considéré résolue la problématique de contrôle des actionneurs pour le suivi d’une trajectoire donnée et nous nous sommes concentrés sur la création d’une trajectoire admissible. Nous avons développé une technique de planification locale pour l’évitement d’un d’obstacles non cartographiés. Pour la construction de trajectoire nous avons utilisé des courbes à géométrie connue et avons montré qu’en utilisant trois clothoïdes et éventuellement deux arcs de cercle (si le braquage maximal est atteint) il est possible de créer des trajectoires à courbure continue adaptées à notre situation. Nous avons montré que l’utilisation d’une carte d’obstacles nous permet de prédire plus en avance de la possibilité d’emprunter un certain couloir de navigation. Chacune des parties de ce travail a fait l’objet de validations en simulation mais aussi sur des données réelles démontrant la pertinence des approches proposées quant à l’application visée. / This work covers the conception of a system capable to do automatic parking maneuvers more versatile than those already commercialized, respecting the technical definition of exteroceptive sensors limited by costs and weight. A typical use case is to set a vehicle to park autonomously in the parking lot of a home, function generally called autonomous home valet parking. Taking from the existing and knowing the expected performances, a system architecture and a functional architecture were drawn. This allowed to compose an assembly of interconnected functions that participated in the creation of modular software architecture, as well as in the creation of connection interfaces with the prototype vehicle. First, we explored the obstacle detection problem. Having a closed property system with ultrasonic sensors, we managed to build an obstacle map with a higher precision level than the build-in product. An increasing limit detection of the ultrasonic sensors was developed using the Structure from Motion technique. This obstacle occupancy information was exploited afterwards in order to solve the detection problem of the navigation corridors. Second, the vehicle localization is addressed. Three localization techniques work for a continuous functioning robustness: the localization by odometry, the localization by occupancy grid map matching and the localization by comparing the current image with the images stored in a database adapted to our needs and improved by computing means. Last, we interested in the vehicle navigation problem. We considered solved the actuator control problem for the tracking of a given trajectory and we concentrated on an admissible trajectory planning. We developed a local path planning technique for avoiding the unmapped obstacles. In order to build the trajectory we used curves of known geometry and we proved that by using clothoides and eventually two circle arches (if maximum steering angle achieved) it is therefore be possible to create trajectories with continuous curves adapted to our situation. We confirmed that using an obstacle map will allow us to predict forehead the possibility to take a specific navigation corridor. Each part of this work was validated in simulation as well as on real data, proving the pertinence of the proposed approaches for the intended application.

Cars are becoming increasingly automated and expected to become fully autonomous in the near future. How will this a ect the car and its position of a symbol of freedom? This thesis investigates how an autonomous vehicle can evolve this symbolic value and be adapted to the use case of an explorative road trip. Based on learnings from travellers and experts the starting point has been the positive experience of a road trip in a conventional vehicle. The target has been to enhance the current experience and create an even more spontaneous and explorative atmosphere with the help of a future scenario and emerging technology. This project gives an example of an interface that supports and en- courages spontaneity which lets the travellers direct and control the vehicle intuitively in order to explore and enjoy what they nd during their journey. Furthermore it extends the travel experience beyond what a road trip has been by connecting travellers to locals.