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La présence de systèmes et d'objets embarqués communicants dans notre vie quotidienne nous a apporté une myriade d'avantages, allant de l'ajout de commodité et de divertissement à l'amélioration de la sûreté de nos déplacements et des soins de santé. Cependant, les défauts et les vulnérabilités de ces systèmes exposent leurs utilisateurs à des risques de dommages matériels, de pertes financières, et même des dommages corporels. Par exemple, certains véhicules commercialisés, qu'ils soient connectés ou conventionnels, ont déjà souffert d'une variété de défauts de conception entraînant des blessures et la mort. Dans le même temps, alors que les véhicules sont de plus en plus connectés (et dans un avenir proche, autonomes), les chercheurs ont démontré la possibilité de piratage de leurs capteurs ou de leurs systèmes de contrôle interne, y compris l'injection directe de messages sur le bus CAN.Pour assurer la sûreté des utilisateurs et des passants, il faut considérer plusieurs facteurs. La sûreté conventionnelle suggère qu'un système ne devrait pas contenir de défauts logiciels et matériels qui peuvent l'empêcher de fonctionner correctement. La "sûreté de la fonction attendue" consiste à éviter les situations que le système ou ses composants ne peuvent pas gérer, comme des conditions environnementales extrêmes. Le timing peut être critique pour certains systèmes en temps réel, car afin d'éviter des situations dangereuses, le système devra réagir à certains événements, comme l'évitement d'obstacles, dans un délai déterminé. Enfin, la sûreté d'un système dépend de sa sécurité. Un attaquant qui peut envoyer des commandes fausses ou modifier le logiciel du système peut changer son comportement et le mettre dans diverses situations dangereuses. Diverses contre-mesures de sécurité et de sûreté pour les systèmes embarqués, en particulier les véhicules connectés, ont été proposées. Pour mettre en oeuvre correctement ces contre-mesures, il faut analyser et vérifier que le système répond à toutes les exigences de sûreté, de sécurité et de performance, et les faire la plus tôt possible dans les premières phases de conception afin de réduire le temps de mise sur le marché, et éviter les reprises. Cette thèse s'intéresse à la sécurité et la sûreté des les systèmes embarqués, dans le contexte du véhicule autonome de l'Institut Vedecom. Parmi les approches proposées pour assurer la sûreté et la sécurité des les systèmes embarqués, l'ingénierie dirigée par modèle est l'une de ces approches qui couvre l'ensemble du processus de conception, depuis la définition des exigences, la conception du matériel et des logiciels, la simulation/vérification formelle et la génération du code final. Cette thèse propose une méthodologie de modélisation pour une conception sûre et sécurisée, basée sur la méthodologie SysML-Sec, qui implique de nouvelles méthodes de modélisation et de vérification. La modélisation de la sécurité est généralement effectuée dans les dernières phases de la conception. Cependant, la sécurité a un impact sur l'architecture/allocation; les décisions de partitionnement logiciel/matériel devraient être prises en fonction de la capacité de l'architecture à satisfaire aux exigences de sécurité. Cette thèse propose comment modéliser les mécanismes de sécurité et l'impact d'un attaquant dans la phase de partitionnement logiciel/matériel. Comme les protocoles de sécurité ont un impact négatif sur le performance d'un système, c'est important de mesurer l'utilisation des composants matériels et les temps de réponse du système. Des composants surchargés peuvent entraîner des performances imprévisibles et des retards indésirables. Cette thèse traite aussi des mesures de latence des événements critiques pour la sécurité, en se concentrant sur un exemple critique pour les véhicules autonomes : le freinage/réponse après la détection d'obstacles. Ainsi, nos contributions soutiennent la conception sûre et sécurisée des systèmes embarqués. / The presence of communicating embedded systems/IoTs in our daily lives have brought a myriad of benefits, from adding conveniences and entertainment, to improving the safety of our commutes and health care. However, the flaws and vulnerabilities in these devices expose their users to risks of property damage, monetary losses, and personal injury. For example, consumer vehicles, both connected and conventional, have succumbed to a variety of design flaws resulting in injuries and death. At the same time, as vehicles are increasingly connected (and in the near future, autonomous), researchers have demonstrated possible hacks on their sensors or internal control systems, including direct injection of messages on the CAN bus.Ensuring the safety of users or bystanders involves considering multiple factors. Conventional safety suggests that a system should not contain software and hardware flaws which can prevent it from correct function. `Safety of the Intended Function' involves avoiding the situations which the system or its components cannot handle, such as adverse extreme environmental conditions. Timing can be critical for certain real-time systems, as the system will need to respond to certain events, such as obstacle avoidance, within a set period to avoid dangerous situations. Finally, the safety of a system depends on its security. An attacker who can send custom commands or modify the software of the system may change its behavior and send it into various unsafe situations. Various safety and security countermeasures for embedded systems, especially connected vehicles, have been proposed. To place these countermeasures correctly requires methods of analyzing and verifying that the system meets all safety, security, and performance requirements, preferably at the early design phases to minimize costly re-work after production. This thesis discusses the safety and security considerations for embedded systems, in the context of Institut Vedecom's autonomous vehicle. Among the proposed approaches to ensure safety and security in embedded systems, Model-Driven Engineering is one such approach that covers the full design process, from elicitation of requirements, design of hardware and software, simulation/formal verification, and final code generation. This thesis proposes a modeling-based methodology for safe and secure design, based on the SysML-Sec Methodology, which involve new modeling and verification methods. Security modeling is generally performed in the last phases of design. However, security impacts the early architecture/mapping and HW/SW partitioning decisions should be made based on the ability of the architecture to satisfy security requirements. This thesis proposes how to model the security mechanisms and the impact of an attacker as relevant to the HW/SW Partitioning phase. As security protocols negatively impact performance, it becomes important to measure both the usage of hardware components and response times of the system. Overcharged components can result in unpredictable performance and undesired delays. This thesis also discusses latency measurements of safety-critical events, focusing on one critical to autonomous vehicles: braking as after obstacle detection. Together, these additions support the safe and secure design of embedded systems.

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With the improvements of the vehicle technology related with connectivity, sharing, automation and electrification and as a solution to the problems that cities are facing, such as an intense population growth and pollution, there are new forms of mobility that are or will be created within the framework of the future mobility. In this context, the arrival of driverless autonomous vehicles will provoke an irreversible change supporting the implementation of new forms of mobility or improving the existent. One factor that will help to do feasible the improvement of the existent mobility is the reduction of costs due to the arrival of autonomous vehicles, what will make ondemand transportation competitive under certain circumstances when comparing costs between it and fixed route systems. This thesis studies for the case of the metro/rail corridors in the metropolitan area of Stockholm which areas are suitable to implement Demand Responsive Transport (DRT) according to urban configuration and access to transit parameters. Once the identification is done, a model to compare between two different operating strategies for feeder services is applied to obtain which one is optimal under different stages of development of the technology related with the vehicles in the fields of automation and electrification. The model used, with additions to existing ones to adapt it to the use of it to real scenarios, gives numerical results for the four considered stages, showing the importance of the travel demand and the street sinuosity on the results and selection of the optimal. The method and criteria developed contributes to have a clear identification of the areas in which the implementation of the DRT services would be feasible in a future mobility scheme.

Autonomous vehicles, commonly known as “self-driving cars”, are increasingly becoming of interest for researchers due to their potential to mitigate traffic accidents and congestion. Using reinforcement learning, previous research has demonstrated that a DQN agent can be trained to effectively navigate a simulated two-lane environment via cooperative driving, in which a model of V2X technology allows an AV to receive information from surrounding vehicles (termed Primary Perceived Vehicles) to make driving decisions. Results have demonstrated that the DQN agent can learn to navigate longitudinally and laterally, but with a prohibitively high collision rate of 1.5% - 4.8% and an average speed of 13.4 m/s. In this research, the impact of including information from traffic vehicles that are outside of those that immediately surround the AV (termed Secondary Perceived Vehicles) as inputs to a DQN agent is investigated. Results indicate that while including velocity and distance information from SPVs does not improve the collision rate and average speed of the driving algorithm, it does yield a lower standard deviation of speed during episodes, indicating lower acceleration. This effect, however, is lost when the agent is tested under constant traffic flow scenarios (as opposed to fluctuating driving conditions). Taken together, it is concluded that while the SPV inclusion does not have an impact on collision rate and average speed, its ability to achieve the same performance with lower acceleration can significantly improve fuel economy and drive quality. These findings give a better understanding of how additional vehicle information during cooperative driving affects automated driving. / Thesis / Master of Applied Science (MASc)

This paper proposes a model to ensure safe and realistic human-robot interaction for an autonomous vehicle interacting with a human-driven vehicle, by incorporating the driving style of the human driver. The interaction is modeled as a game, where both agents try to maximize future rewards. The driving style of the human is captured via the role of the human driver in the game, capturing the fact that humans with different driving styles reason differently. The solution of the game is obtained using an numerical approximation and used by the autonomous vehicle to plan optimally ahead. The model is validated via simulations on a safety-critical scenario, where realistic driving style-dependent behaviour emerges naturally.

The state of the art of autonomous vehicles requires operators to remain vigilant while performing secondary tasks. The goal of this research was to investigate how dynamically allocated secondary tasks affected driving performance, cognitive load, and situation awareness. Secondary tasks were presented at rates based on the autonomy level present and whether the autonomous system was engaged. A rapid secondary task rate was also presented for two short periods regardless of whether autonomy was engaged. There was a three-minute familiarization phase followed by a data collection phase where participants responded to secondary tasks while preventing the vehicle from colliding into random obstacles. After data collection, there was a brief survey to gather data on cognitive load, situation awareness, and relevant demographics. The data was compared to data gathered in a similar study by Cossitt [10] where secondary tasks were presented at a controlled frequency and a gradually increasing frequency.

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A transformative revolution in transportation is coming with the advent of Au-tonomous Vehicles (AVs), which are expected to increase mobility, reduce trafficcongestion, and save fuel. Although AVs present significant advantages, they alsopose substantial challenges, particularly when it comes to security and safety. Theaim of this study is to map out the existing knowledge in order to facilitate furtherresearch and development, which will hasten the rollout of secure and reliable au-tonomous vehicles. This, in turn, will enable a sustainable and efficient future fortransportation. Research on AV safety and security is reviewed in this thesis in acomprehensive systematic literature review. The search process identified a total of283 studies published between 2019 and 2022, out of which 24 studies were selectedthrough a multi-stage process according to our predefined protocol. Based on re-search topics in selected studies, our findings have a significant impact on the fieldof Artificial Intelligence and automated vehicles. Based on our findings, we canprovide a summary of current knowledge regarding the safety, security, and stabilityimplications of autonomous vehicles. Simulations, real-life experiments, and physi-cal tests were all used in the selected articles for evaluation. Aside from the excellentresults, we identified many limitations of the articles, including the limitations of thedata sets, the analysis of unusual events, and the verification practices.

We review recent research into trajectory planning for autonomous overtaking to understand existing challenges. Then, the recently developed framework Learning Model Predictive Control (LMPC) is presented as a suitable method to iteratively improve an overtaking manoeuvre each time it is performed. We present recent extensions to the LMPC framework to make it applicable to overtaking. Furthermore, we also present two alternative modelling approaches with the intention of reducing computational complexity of the optimization problems solved by the controller. All proposed frameworks are built from scratch in Python3 and simulated for evaluation purposes. Optimization problems are modelled and solved using the Gurobi 9.0 Python API gurobipy. The results show that LMPC can be successfully applied to the overtaking problem, with improved performance at each iteration. However, the first proposed alternative modelling approach does not improve computational times as was the intention. The second one does but fails in other areas. / Vi går igenom ny forskning inom trajectory planning för autonom omkörning för att förstå de utmaningar som finns. Därefter föreslås ramverket Learning Model Predictive Control (LMPC) som en lämplig metod för att iterativt förbättra en omkörning vid varje utförande. Vi tar upp utvidgningar av LMPC-ramverket för att göra det applicerbart på omkörningsproblem. Dessutom presenterar vi också två alternativa modelleringar i syfte att minska optimeringsproblemens komplexitet. Alla tre angreppssätt har byggts från grunden i Python3 och simulerats i utvärderingssyfte. Optimeringsproblem har modellerats och lösts med programvaran Gurobi 9.0s python-API gurobipy. Resultaten visar att LMPC kan tillämpas framgångsrikt på omkörningsproblem, med förbättrat utförande vid varje iteration. Den första alternativa modelleringen minskar inte beräkningstiden vilket var dess syfte. Det gör däremot den andra alternativa modelleringen som dock fungerar sämre i andra avseenden.​