The manufacturing of connected and automated vehicles (CAVs) is happening and they are aiming at providing an efficient, safe, and seamless driving experience. This is done by offering automated driving together with wireless communication to and from various objects in the surrounding environment. How automated the vehicle is can be classified from level 0 (no automation at all) to level 5 (fully automated). There is many potential attack vectors of CAVs for attackers to take advantage of and these attack vectors may change depending on what level of automation the vehicle have. There are some known vulnerabilities of CAVs where the security has been breached, but what is seemed to be lacking in the academia in the field of CAVs is a place where the majority of information regarding known attack vectors and cyber-attacks on those is collected. In addition to this the attack vectors may be analyzed for each level of automation the vehicles may have. This research is a systematic literature review (SLR) with three stages (planning, conducting, and report) based on literature review methodology presented by Kitchenham (2004). These stages aim at planning the review, finding articles, extracting information from the found articles, and finally analyzing the result of them. The literature review resulted in information regarding identified cyberattacks and attack vectors the attackers may use as a path to exploit vulnerabilities of a CAV. In total 24 types of attack vectors were identified. Some attack vectors like vehicle communication types, vehicle applications, CAN bus protocol, and broadcasted messages were highlighted the most by the authors. When the attack vectors were analyzed together with the standard of ‘Levels of Driving Automation’ it became clear that there are more vulnerabilities to consider the higher level of automation the vehicle have. The contributions of this research are hence (1) a broad summary of attack vectors of CAVs and (2) a summary of these attack vectors for every level of driving automation. This had not been done before and was found to be lacking in the academia.

As cycling becomes more popular and automated driving is on the rise, it can be assumed that in the city of the future highly automated vehicles (HA Vs) and cyclists will share the same roads. Yet only little is known about how cyclists announce their maneuvers to motorized vehicles or how they communicate and interact with them. Knowledge on these aspects is currently missing to guide the design of cyclist-HA V interactions. Situations where a cyclist rides upfront a vehicle, will be especially challenging for HA Vs, such as when a cyclist (A) avoids an obstacle on the road section ahead, (B) merges onto the road from an ending cycling path, or (C) leaves the road turning into a driveway {see Figure 1) [1 ]. Based on the cyclist's intention, the HA V will have to pass or keep following with only limited options to communicate to the cyclist ahead. Design solutions derived from the well-studied field of pedestrian-HA V interactions cannot simply be transferred to the here considered cyclist-HA V interactions, since in past research successful design concepts for pedestrians were not beneficial for cyclists [2]. Hence, it is vital to investigate the behavior and experiences of cyclists in more detail and to explore possible design solutions for HA V interaction behavior in these situations. With this study we aim to get more insights into the subjective experience of cyclists travelling in longitudinal traffic, especially during cyclist-vehicle interactions, as well as to derive cyclists' requirements to design safe and desirable cyclist-HA V interactions.

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L’estimation des matrices origine-destination (OD) est un sujet de recherche important depuis les années 1950. En effet, ces tableaux à deux entrées recensent la demande de transport d'une zone géographique donnée et sont de ce fait un élément clé de l'ingénierie du trafic. Historiquement, les seules données disponibles pour leur estimation par les statistiques étaient les comptages de véhicules par les boucles magnétiques. Ce travail s'inscrit alors dans le contexte de l'installation à Brisbane de plus de 600 détecteurs Bluetooth qui ont la capacité de détecter et d'identifier les appareils électroniques équipés de cette technologie.Dans un premier temps, il explore la possibilité offerte par ces détecteurs pour les applications en ingénierie du transport en caractérisant ces données et leurs bruits. Ce projet aboutit, à l'issue de cette étude, à une méthode de reconstruction des trajectoires des véhicules équipés du Bluetooth à partir de ces seules données. Dans un second temps, en partant de l'hypothèse que l'accès à des échantillons importants de trajectoires va se démocratiser, cette thèse propose d'étendre la notion de matrice OD à celle de matrice OD par lien afin de combiner la description de la demande avec celle de l'utilisation du réseau. Reposant sur les derniers outils méthodologies développés en optimisation convexe, nous proposons une méthode d'estimation de ces matrices à partir des trajectoires inférées par Bluetooth et des comptages routiers.A partir de peu d'hypothèses, il est possible d'inférer ces nouvelles matrices pour l'ensemble des utilisateurs d'un réseau routier (indépendamment de leur équipement en nouvelles technologies). Ce travail se distingue ainsi des méthodes traditionnelles d'estimation qui reposaient sur des étapes successives et indépendantes d'inférence et de modélisation. / Origin Destination matrix estimation is a critical problem of the Transportation field since the fifties. OD matrix is a two-entry table taking census of the zone-to-zone traffic of a geographic area. This traffic description tools is therefore paramount for traffic engineering applications. Traditionally, the OD matrix estimation has solely been based on traffic counts collected by networks of magnetic loops. This thesis takes place in a context with over 600 Bluetooth detectors installed in the City of Brisbane. These detectors permit in-car Bluetooth device detection and thus vehicle identification.This manuscript explores first, the potentialities of Bluetooth detectors for Transport Engineering applications by characterising the data, their noises and biases. This leads to propose a new methodology for Bluetooth equipped vehicle trajectory reconstruction. In a second step, based on the idea that probe trajectories will become more and more available by means of new technologies, this thesis proposes to extend the concept of OD matrix to the one of link dependent origin destination matrix that describes simultaneously both the traffic demand and the usage of the network. The problem of LOD matrix estimation is formulated as a minimisation problem based on probe trajectories and traffic counts and is then solved thanks to the latest advances in nonsmooth convex optimisation.This thesis demonstrates that, with few hypothesis, it is possible to retrieve the LOD matrix for the whole set of users in a road network. It is thus different from traditional OD matrix estimation approaches that relied on successive steps of modelling and of statistical inferences.

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