Vehicle Action Intention Prediction plays a more and more crucial role in automated driving and traffic safety. It allows automated vehicles to comprehend the other road participants’ current actions, and foresee the upcoming actions, which can significantly reduce the likelihood of traffic accidents, so as to enhance overall road safety. Meanwhile, by anticipating other vehicles’ movements on the road, the ego vehicle can plan its velocity and trajectory in advance, and make more smooth and finer adjustments during the whole driving process, contributing to a more safe and efficient traffic. Furthermore, the intention prediction enables vehicles to respond proactively rather than reactively in traditional ADAS (Advanced Driver Assistance Systems), such as AEB (Automatic Emergency Braking), which facilitates a more preventive and early intervention approach to traffic safety. In normal conditions, traffic behavior is controlled by traffic rules. This thesis explores vehicle behavior using intention prediction models in scenarios where there are no traffic rules. At hand, we have a unique dataset containing vehicle trajectories, collected from 2 cameras installed overhead on a 1-lane narrowing street, where the vehicles need to negotiate their right of way. After pre-processing these data to form specific input structures, we use different classifier models including both traditional methods and deep learning methods to make vehicle action intention predictions. The data was organized in 3-second windows and contained vehicle position and distance to the center of the intersection along with the speed of both vehicles. We compared and evaluated the model performances and found that MLPs (Multi-Layer Perceptrons) and LSTM (Long Short Term Memory) yield the best performance. Furthermore, a feature selection method and features’ importance analysis are also applied to explore which variables influence the model most in order to explain the internal principle of the prediction model. It was found that close to the narrowing street the first and last samples of the position and distance in the time window and the last sample of the speed of both vehicles were found to influence the model performance the most. Further away from the narrowing street, the first and last samples of the position of the vehicle have a higher influence on the model.

The development of autonomous vehicles is progressing rapidly through extensive efforts by the automotive industry and researchers. One of the key factors for the adoption of autonomous driving technology is motion comfort and the ability to engage in non-driving tasks such as reading, socialising, and relaxing without experiencing motion sickness while travelling. Therefore, for the full success of autonomous vehicles, it is necessary to learn how to design and control the vehicles to mitigate motion sickness for the passengers.  This thesis aims to investigate methods for prediction of motion sickness in autonomous vehicles and how to mitigate it using vehicle dynamics based solutions, with an emphasis on trajectory planning. As a first step, a review and evaluation of existing motion sickness prediction methods were performed. The review highlighted the importance of accurate motion sickness assessment in the early phases of autonomous vehicle design. Two chosen methods (ISO 2631-based and sensory conflict theory-based) were evaluated to estimate individual motion sickness feelings using measured data and subjective assessment ratings from field tests. It can be concluded that the methods can be adjusted to predict individual motion sickness feelings, as shown by the comparison with the experimental data. To continue the work, a review of vehicle dynamics based motion sickness mitigation methods for autonomous vehicles was performed. Several chassis control strategies in literature like active suspension, rear-wheel steering and torque distribution have demonstrated the potential help to reduce motion sickness. Another effective approach to mitigate motion sickness in autonomous vehicles is to regulate vehicle speed and path using trajectory planning which was chosen to be further investigated. The trajectory planning was constructed as an optimisation problem where there is a trade-off between motion sickness and manoeuvre time. The impact of the trajectory planning algorithm to reduce motion sickness was analysed by simulating two different vehicle models in specific test manoeuvres. The results indicate that driving style has a significant influence on motion sickness and trajectory planning algorithms should be carefully designed to find a good balance between journey time and motion sickness. The research presented in this thesis contributes to the development of methodologies for predicting and mitigating motion sickness in autonomous vehicles, helping to achieve the goal of ensuring their overall success. / Utvecklingen av autonoma fordon går snabbt framåt tack vare omfattande insatser från fordonsindustrin och forskare. En av de viktigaste faktorerna för införandet av teknik för autonom körning är åkkomfort och möjligheten att ägna sig åt andra saker än körning, som att läsa, umgås och koppla av, utan att drabbas av åksjuka under resan. För att autonoma fordon ska lyckas fullt ut är det därför nödvändigt att förstå hur man utformar och styr fordonen för att minska risken för att passagerarna drabbas av åksjuka.  Denna licentiatuppsats syftar till att undersöka hur åksjuka kan förutsägas i vägfordon och hur den kan reduceras med hjälp av fordonsdynamikbaserade lösningar, med tonvikt på trajektorieplanering. Som ett första steg genomfördes en granskning och utvärdering av befintliga metoder för åksjukeprediktion. Granskningen belyste vikten av en korrekt bedömning av åksjuka i de tidiga faserna av autonom fordonsdesign. Två valda metoder (ISO 2631-baserad och sensorisk konfliktbaserad) utvärderades för att uppskatta individuell åksjuka med hjälp av uppmätta data och subjektiva bedömningar från fälttester. Slutsatsen är att metoderna kan justeras för att förutsäga individuell åksjuka, vilket framgår av jämförelsen med experimentella data. För att fortsätta arbetet gjordes en genomgång av fordonsdynamikbaserade metoder för att minska åksjuka i autonoma fordon. Flera chassireglerstrategier i litteraturen, såsom aktiv fjädring, bakhjulsstyrning och drivmomentfördelning, har visat sig kunna bidra till att minska åksjuka. En annan effektiv metod för att minska åksjuka i autonoma fordon är att reglera fordonets hastighet och bana med hjälp av trajektorieplanering, vilket valdes att undersökas ytterligare. Trajektorieplaneringen konstruerades som ett optimeringsproblem där det finns en avvägning mellan åksjuka och manövertid. Effekten av trajektorieplaneringsalgoritmen för att minska åksjuka analyserades genom att simulera två olika fordonsmodeller i specifika testmanövrar. Resultaten indikerar att körstil har en betydande inverkan på åksjuka och att algoritmer för trajektorieplanering bör utformas noggrant för att hitta en bra balans mellan restid och åksjuka. Forskningen som presenteras i denna uppsats bidrar till utvecklingen av metoder för att förutsäga och mildra åksjuka i autonoma fordon, vilket hjälper till att uppnå målet att säkerställa deras framgång.

With the development of Autonomous Driving (AD) technology, there is a growing concern over the safety of the technology. Finding methods to improve the reliability of this technology becomes a current challenge. The AD system is composed of a perception module, a planning module, and a control module. The perception module, which provides information about the environment for the whole system, is a critical part of the AD system. This project aims to provide a better understanding of the functionality and reliability of the perception module of an AD system. In this project, a simple model of the perception module is built with YOLOv5-nano for object detection, StrongSORT for object tracking, and MonoDepth2 for depth estimation. The system takes images from a single camera as input and produces a time series of distance to the preceding vehicle. Fault injection technologies are utilized for testing the reliability of the system. Different faults, including weather factors, sensor faults, and encoder faults, are injected. The system behaviors under faults are observed and analyzed. Then multiple methods for anomaly detection are applied to the time series of distance data, including the statistic method ARIMA, and the machine learning methods MLP and LSTM. Comparisons are made among the anomaly detection methods, based on the efficiency and performance. The dataset in this project is generated by the CARLA simulator. / Med utvecklingen av tekniken för autonom körning (AD) växer oro över teknologins säkerhet. Att hitta metoder för att förbättra tillförlitligheten hos denna teknologi blir en aktuell utmaning. AD-systemet består av en perceptionsmodul, en planeringsmodul och en styrmodul. Perceptionsmo­dulen, som tillhandahåller information om miljön för hela systemet, är en kritisk del av AD-systemet. Detta projekt syftar till att ge en bättre förståelse för funktionaliteten och tillförlitligheten hos perceptionsmodulen i ett AD-system. I detta projekt byggs en enkel modell av perceptionsmodulen med YOLOv5-nano för objektdetektion, StrongSORT för objektföljning och MonoDepth2 för djupuppskattning. Systemet tar bilder från en enda kamera som inmatning och producerar en tidsserie av avståndet till det föregående fordonet. Felinjektionstekniker används för att testa systemets tillförlitlighet. Olika fel, inklusive väderfaktorer, sensorfel och maskininlärningsfel, injiceras. Systemets beteende under fel observeras och analyseras. Därefter tillämpas flera metoder för avvikelsedetektering på tidsserien av avstånd, inklusive statistikmetoden ARIMA samt maskininlärningsmetoderna MLP och LSTM. Jämförelser görs mellan avvikelsedetekteringsmetoderna, baserat på effektivitet och prestanda. Datamängden i detta projekt genereras av CARLA­simulatorn.

Autonomous driving could bring profound benefits for our society. The benefits range from economic and safety benefits due to the reduction of the number of traffic accidents, to environmental gains due to reduced traffic congestion. However, the utopian future of self-driving vehicles is yet to come. To this end, we propose machine learning methods to address three pivotal aspects of autonomous driving: visual privacy, 3D depth perception, and trajectory prediction modeling. We begin by exploring the crucial issue of visual privacy within person-aware visual systems. We propose the use of depth-information to protect privacy in person-aware visual systems while preserving important foreground subjects and scene structures. We aim to preserve the identity of foreground subjects while hiding superfluous details in the background that may contain sensitive information. In particular, for an input color and depth image pair, we first create a sensitivity map which favors background regions (where privacy should be preserved) and low depth-gradient pixels (which often relates a lot to scene structure but little to identity). We then combine this per-pixel sensitivity map with an inhomogeneous image obscuration process for privacy protection. We tested the proposed method using data involving different scenarios including various illumination conditions, various number of subjects, different context, etc. The experiments demonstrate the quality of preserving the identity of humans and edges obtained from the depth information while obscuring privacy intrusive information in the background. Next, we focus on the label layout problem: AR technologies can overlay virtual annotations directly onto the real-world view of a self-driving vehicle (SDV). Autonomous vehicles operate in dynamic environments, due to the complexity of the traffic scene and the interactions between the participating agents. Overlaying virtual annotations directly onto the real-world view of a SDV, can provide additional context, such as highlighting important information or projecting the future trajectories of other participants. Designing a layout of labels that does not violate domain-specific design requirements, while at the same time satisfying aesthetic and functional principles of good design, can be a daunting task even for skilled visual designers. Presenting the annotations in 3D object space instead of projection space, allows for the preservation of spatial and depth cues. This results in stable layouts in dynamic environments, since the annotations are anchored in 3D space. In this domain, we make two major contributions. First, we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space. For this, we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space. Second, we introduce PartLabeling: an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models. This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available, as well as ensure research reproducibility. The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset consisting of models suitable for the label management task. The dataset consists of 1,000 3D models with part annotations. Finally, we focus on the trajectory prediction task in the context of autonomous driving. Predicting the trajectories of multiple participating agents in the context of autonomous driving is a challenging problem due to the complexity of the traffic scene and the interactions between the agents. Autonomous vehicles need to effectively anticipate the behavior of other movingparticipants in the traffic scene (human pedestrians, cyclists, animals, other moving vehicles). The task of modeling human driver behavior, as well as the interactions between the traffic participants must be addressed to enable a safe and optimized autonomous vehicle systems. There are many factors that traffic participants take into consideration in order to safely interact with other traffic participants. Human drivers have sophisticated interaction strategies that come naturally to them. Given the highly interactive nature of traffic scenarios, representing the interactions between multiple participating agents in a traffic scene in the form of a graph structure is a natural conclusion. In order to leverage the influences between multiple agents in a traffic scene, we structure the scene as a graph whose nodes represent the traffic participants. The node features are each agent’s surrounding context encoded as a raster image. For this purpose, we leveragel R-GCN (Relational Graph-Convolutional Netowrks). Then, we propose a novel Cross-Modal Attention Network (CMAN) to encourage interactions between two modalities: 1) the latent features of an ego-agent’s raster image and 2) the latent features of the surrounding agents’ influences on the ego-agent, in a manner that allows these two modalities to complement each other. / Computer and Information Science

Målet med examensarbetet har varit att samla information åt ett tekniskt konsultföretag för att öka deras kunskap om autonoma system och fordonskommunikation. Statusen på arbetet kring dessa aktiva säkerhetssystem hos olika aktörer och hur systemen implementeras i dagens och framtidens fordon har undersökts genom omfattande litteraturstudier, intervjuer och marknadsanalyser. De autonoma systemen kan samla information från omgivningen genom sensorer och bidra till ett jämnare trafikflöde, ökad säkerhet, lättare bilar och bättre miljö. Genom fordonskommunikationen kan fordon kommunicera med varandra samt infrastrukturen och garantera en säker bilfärd. År 2030 utgörs innerstaden av autonom, elektrifierad kollektivtrafik för att transportera människor på begäran, samtidigt som personbilar till viss del förbjuds. Potentiella behov för människan i en fullt autonom bil har identifierats och diverse produktutvecklingsmetoder har tillämpats för att utforma två konceptuella lösningar för en framtida bilinteriör. Lösningarna visar interaktionen mellan människa och system eftersom underhållning och bekvämlighet blir viktigt i en fullt autonom bil. Respektive lösning är statsägd och rymmer fyra passagerare. I lösningarna är sittplatserna placerade på ett sätt som underlättar kommunikation mellan passagerarna. Passagerarna kan underhållas eller informeras individuellt eller gemensamt via text, ljud och bild. / The goal with this thesis project has been to collect information for a technical consulting company in order to increase their knowledge about autonomous systems and vehicular communication. The status of how various operators work with active safety systems and how the systems are implemented in current and future vehicles has been investigated through extensive literature studies, interviews and market research. The autonomous systems can collect information from the surrounding through sensors and contribute to better traffic efficiency, increased safety, lighter cars and a better environment. Through vehicle communication, the vehicle can communicate with each other in order to guarantee a safe ride. In 2030 the inner city constitutes of autonomous, electrified public transport to transport people on demand, meanwhile private cars are prohibited. Potential needs for the human in a fully, autonomous car has been identified and various product development methods has been applied in order to develop two conceptual solutions for a future car interior. The solutions show the interaction between human and system since entertainment and comfort becomes important in a fully, autonomous car. Each solution is state-owned and holds four passengers. In the solutions, the seats are placed in regard to facilitate communication between the passengers. The passengers can be entertained or informed individually or collectively by text, sound and images.

Autonomous driving is increasingly popular among people and automotive industries in realizing their presence both in passenger and goods transportation. Safer autonomous navigation might be very challenging if there is a failure in sensing system. Among several sensing systems, image classification plays a major role in understanding the road signs and to regulate the vehicle control based on urban road rules. Hence, a robust classifier algorithm irrespective of camera position, view angles, environmental condition, different vehicle size & type (Car, Bus, Truck, etc.,) of an autonomous platform is of prime importance. In this study, Convolutional Neural Network (CNN) based classifier algorithm has been implemented to ensure improved robustness for recognizing traffic signs. As training data play a crucial role in supervised learning algorithms, there come an effective dataset requirement which can handle dynamic environmental conditions and other variations caused due to the vehicle motion (will be referred as challenges). Since the collected training data might not contain all the dynamic variations, the model weakness can be identified by exposing it to variations (Blur, Darkness, Shadow, etc.,) faced by the vehicles in real-time as a initial testing sequence. To overcome the weakness caused due to the training data itself, an effective augmentation technique enriching the training data in order to increase the model capacity for withstanding the variations prevalent in urban environment has been proposed. As a major contribution, a framework has been developed to identify model weakness and successively introduce a targeted augmentation methodology for classification improvement. Targeted augmentation is based on estimated weakness caused due to the challenges with difficulty levels, only those necessary for better classification were then augmented further. Predictive Augmentation (PA) and Predictive Multiple Augmentation (PMA) are the two proposed methods to adapt the model based on targeted challenges by delivering with high numerical value of confidence. We validated our framework on two different training datasets (German Traffic Sign Recognition Benchmark (GTSRB) and Heavy Vehicle data collected from bus) and with 5 generated test groups containing varying levels of challenge (simple to extreme). The results show impressive improvement by ≈ 5-20% in overall classification accuracy thereby keeping their high confidence.

La voiture autonome pourrait réduire le nombre de morts et de blessés sur les routes tout en améliorant l'efficacité du trafic. Cependant, afin d'assurer leur déploiement en masse sur les routes ouvertes au public, leur sécurité doit être garantie en toutes circonstances. Cette thèse traite de l'architecture de planification et de contrôle pour la conduite automatisée et défend l'idée que l'intention du véhicule doit correspondre aux actions réalisées afin de garantir la sécurité à tout moment. Pour cela, la faisabilité cinématique et dynamique de la trajectoire de référence doit être assurée. Sinon, le contrôleur, aveugle aux obstacles, n'est pas capable de la suivre, entraînant un danger pour la voiture elle-même et les autres usagers de la route. L'architecture proposée repose sur la commande à modèle prédictif fondée sur un modèle bicyclette cinématique afin de planifier des trajectoires de référence sûres. La faisabilité de la trajectoire de référence est assurée en ajoutant une contrainte dynamique sur l'angle au volant, contrainte issue de ces travaux, afin d'assurer que le modèle bicyclette cinématique reste valide. Plusieurs contrôleurs à haute-fréquence sont ensuite comparés afin de souligner leurs avantages et inconvénients. Enfin, quelques résultats préliminaires sur les contrôleurs à base de commande sans modèle et leur application au contrôle automobile sont présentés. En particulier, une méthode efficace pour ajuster les paramètres est proposée et implémentée avec succès sur la voiture expérimentale de l'ENSIAME en partenariat avec le laboratoire LAMIH de Valenciennes. / Autonomous vehicles are believed to reduce the number of deaths and casualties on the roads while improving the traffic efficiency. However, before their mass deployment on open public roads, their safety must be guaranteed at all time.Therefore, this thesis deals with the motion planning and control architecture for autonomous vehicles and claims that the intention of the vehicle must match with its actual actions. For that purpose, the kinematic and dynamic feasibility of the reference trajectory should be ensured. Otherwise, the controller which is blind to obstacles is unable to track it, setting the ego-vehicle and other traffic participants in jeopardy. The proposed architecture uses Model Predictive Control based on a kinematic bicycle model for planning safe reference trajectories. Its feasibility is ensured by adding a dynamic constraint on the steering angle which has been derived in this work in order to ensure the validity of the kinematic bicycle model. Several high-frequency controllers are then compared and their assets and drawbacks are highlighted. Finally, some preliminary work on model-free controllers and their application to automotive control are presented. In particular, an efficient tuning method is proposed and implemented successfully on the experimental vehicle of ENSIAME in collaboration with the laboratory LAMIH of Valenciennes.

Cette thèse de doctorat s’intéresse au suivi de trajectoire basé sur la perception visuelle et la localisation en milieu urbain. L'approche proposée comprend deux systèmes. Le premier concerne la perception de l'environnement. Cette tâche est effectuée en utilisant des techniques d'apprentissage profond pour extraire automatiquement les caractéristiques visuelles 2D et utiliser ces derniers pour apprendre à distinguer les différents objets dans les scénarios de conduite. Trois techniques d'apprentissage approfondi sont adoptées : la segmentation sémantique pour assigner chaque pixel d’une image à une classe, la segmentation d'instance pour identifier les instances séparées de la même classe et la classification d'image pour reconnaître davantage les étiquettes spécifiques des instances. Ici, notre système considère 15 classes d'objets et reconnaît les panneaux de signalisation. Le deuxième système fait référence au suivi de chemin numérisé. Dans un premier temps, le véhicule équipé enregistre d'abord l'itinéraire avec un système de vision stéréo et un récepteur GPS (étape d'apprentissage ou numérisation du chemin). Ensuite, le système proposé analyse hors ligne la trajectoire GPS et identifie exactement les emplacements des courbes dangereuses (brusques) et les limitation de vitesse via les données visuelles. Enfin, une fois que le véhicule est capable de se localiser lui-même durant la phase de suivi de chemin, le module de contrôle du véhicule piloté avec notre algorithme de négociation de vitesse, prend en compte les informations extraites et calcule la vitesse idéale à exécuter. Grâce aux résultats expérimentaux des deux systèmes, nous prouvons que le premier est capable de détecter et de reconnaître précisément les objets d'intérêt dans les scénarios urbains, tandis que le suivi de trajectoire réduit significativement les erreurs latérales entre le trajet appris et le trajet parcouru. Nous soutenons que la fusion des deux systèmes améliorera le suivi de chemin pour prévenir les accidents ou assurer la conduite autonome. / This PhD thesis focuses on developing a path tracking approach based on visual perception and localization in urban environments. The proposed approach comprises two systems. The first one concerns environment perception. This task is carried out using deep learning techniques to automatically extract 2D visual features and use them to learn in order to distinguish the different objects in the driving scenarios. Three deep learning techniques are adopted: semantic segmentation to assign each image pixel to a class, instance segmentation to identify separated instances of the same class and, image classification to further recognize the specific labels of the instances. Here our system segments 15 object classes and performs traffic sign recognition. The second system refers to path tracking. In order to follow a path, the equipped vehicle first travels and records the route with a stereo vision system and a GPS receiver (learning step). The proposed system analyses off-line the GPS path and identifies exactly the locations of dangerous (sharp) curves and speed limits. Later after the vehicle is able to localize itself, the vehicle control module together with our speed negotiation algorithm, takes into account the information extracted and computes the ideal speed to execute. Through experimental results of both systems, we prove that, the first one is capable to detect and recognize precisely objects of interest in urban scenarios, while the path tracking one reduces significantly the lateral errors between the learned and traveled path. We argue that the fusion of both systems will ameliorate the tracking approach for preventing accidents or implementing autonomous driving.

In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match its reported performance when trained and evaluated on the KITTI dataset. The architecture was also found to transfer reasonably well from the synthetic SYN dataset to KITTI, and is thus believed to be usable in a semi-automatic 3D bounding box annotation process. The Frustum-PointNet architecture was also extended to explicitly utilize image features, which surprisingly degraded its detection performance. Furthermore, an image-only 3D object detection model was designed and implemented, which was found to compare quite favourably with current state-of-the-art in terms of detection performance. Additionally, the PixelDA approach was adopted and successfully applied to the MNIST to MNIST-M domain adaptation problem, which validated the idea that unsupervised domain adaptation using Generative Adversarial Networks can improve the performance of a task network for a dataset lacking ground truth annotations. Surprisingly, the approach did however not significantly improve upon the performance of the image-based 3D object detection models when trained on the SYN dataset and evaluated on KITTI.

Les avantages des véhicules autonomes sont formidables, mais le chemin vers une vraie autonomie sera long et semé d’incertitudes. La recherche de ces dernières années s’est basée sur des systèmes multi-capteurs capables de percevoir l’environnement dans lequel le véhicule est conduit. Ces systèmes deviennent plus complexes quand on contrôle le véhicule autonome, différents systèmes de contrôle sont activés dépendant de la décision du système multi-capteurs. Chacun de ces systèmes suit des critères de performance et de stabilité lors de leur conception. Cependant, ils doivent fonctionner ensemble, garantissant une stabilité et étant capable de se charger des changements dynamiques, structuraux et environnementaux. Cette thèse explore la paramétrisation Youla-Kucera (YK) dans des systèmes dynamiques comme les voitures, en insistant sur la stabilité quand la dynamique change, ou que le trafic impose une reconfiguration du contrôleur. Concentrons-nous sur l’obtention de résultats de simulation et expérimentaux en relation avec le "Cooperative Adaptive Cruise Control" (CACC), dans le but, non pas d’utiliser, ici, pour la première fois la paramétrisation YK dans le domaine des systèmes de transport intelligents (STI), mais d’améliorer l’état de l’art en CACC aussi. Des résultats de reconfiguration stable de contrôleurs sont donnés quand la communication avec le véhicule précédent n’est plus disponible, en cas de manœuvre d’entrées/sorties ou lorsqu’ils sont entourés de véhicules aux dynamiques différentes. Ceci démontrant l’adaptabilité, la stabilité et l’implémentation réelle de la paramétrisation YK comme structure générale de contrôle pour les véhicules autonomes. / Benefits of autonomous vehicles are genuinely exciting, but the route to true autonomy in transportation will likely be long and full of uncertainty. Research on the last years is on the development of multi-sensor systems able to perceive the environment in which the vehicle is driving in. These systems increase complexity when controlling an autonomous vehicle, as different control systems are activated depending on the multi-sensor decision system. Each of these systems follows performance and stability criteria for its design, but they all must work together, providing stability guarantees and being able to handle dynamics, structural and environmental changes. This thesis explores the Youla-Kucera (YK) parameterization in dynamics systems such as vehicles, with special emphasis on stability when some dynamics change or the traffic situation demands controller reconfiguration. Focus is in obtaining simulation and experimental results related to Cooperative Adaptive Cruise Control (CACC), with the aim not only of using for the very first time YK parameterization in the Intelligent Transportation Systems (ITS) domain, but improving CACC state-of-the art. Stable controller reconfiguration results are given when non-available communication link with the preceding vehicle, cut-in/out maneuvers or surrounding vehicles with different dynamics, proving adapability, stability and possible real implementation of the YK parameterization as general control framework for autonomous vehicles.