Connected vehicles is a growing market. There are currently several such services available, but many of them are constrained in the sense that they are bound to recently produced cars and either expensive or strongly limited in the services that they provide. In this master thesis we investigate the possibility to implement a generic platform that is of low cost and simple to install in any vehicle, but that still has the ability to provide a wide range of services. It is proposed that a crucial step in such a system is to reconstruct the vehicle’s kinematics, as this enables the possibility to developed a wide range of services by feature extraction and interpret the result from a dynamics perspective. A mathematical model that describes how the kinematics can be reconstructed is proposed, and a filter that performs such reconstruction is implemented. Based on this reconstruction, two filters that interpret the output are implemented as a proof of concept for the proposed mathematical model. The complete implemented filter solution is tested on measurement data from actual driving scenarios and it is seen that we can identify when the vehicle makes a hard turn, and find where the surrounding road conditions are poor. / Uppkopplade fordon är en växande marknad. I dagsläget finns flera sådana tjänster, men ofta är dessa begränsade i den meningen att de antingen endast finns tillgängliga för nyproducerade fordon eller bara erbjuder ett smalt utbud av tjänster. I detta examensarbete undersöker vi möjligheten att utveckla en generisk plattform för uppkopplade fordon som är billig och enkel att installera, men som också kan erbjuda ett stort urval av tjänster. Det föreslås att ett viktigt steg i en sådan lösning är att rekonstruera fordonets kinematik, då detta möjliggör utvecklandet av ett brett urval av tjänster genom att identifiera karakteristiska egenskaper i kinematiken, samt göra tolkningar utifrån dynamikbetraktelser. En matematisk modell för att beskriva hur kinematiken kan rekonstrueras från givna indata presenteras, och ett filter som utför denna rekonstruktion implementeras. Ytterligare två filter implementeras för att påvisa att den rekonstruerade kinematiken samt den föreslagna matematiska modellen kan användas till att identifiera olika scenarion ur verkligheten. Den kompletta filterlösningen testas på mätdata från faktiska körningar och vi ser att vi kan identifiera när fordonet gör skarpa svängar, samt när vägförhållandena är dåliga.

With developing technologies and growing infrastructures, connected experiences are expanding their realms towards various devices and scenarios in our lives. One of the areas, which is going under a big change due to this connectivity is the car related experiences. As connectivity is intrinsically enabler of different experiences and services, it is foreseen that it will bring a different dimension to car and driving related experiences as well.By investigating the future trends and possibilities that connectivity can provide to car and driving related experiences, this thesis aims for imagining the near future scenarios with an explorative approach, focusing on one and addressing to the rising issues with a design proposal that is meaningful to both users and the industry.The result, Tripcloud, contributes to the future scenario of having a road trip with the car, with a new digital platform that aims for supporting the users throughout the planning and bringing the plans into the car experience seamlessly and safely. It aims for reducing today’s existing complexity in terms of interaction and cognition to provide a better experience and avoid driver distraction. With providing organised information pieces, information exchange between people and automated links with mobile devices and car, Tripcloud offers easier an more convenient alternative for road trip planing and bringing the plans into car experiences for the near future.

The connected car segment has high demands on the exchange of data between the car on the road, and a variety of services in the backend. By the end of 2020, connected services will be mainstream automotive offerings, according to Telefónica - Connected Car Industry Report 2014 the overall number of vehicles with built-in internet connectivity will increase from 10% of the overall market today to 90% by the end of the decade [1]. Connected car solutions will soon become one of the major business drivers for the industry; they already have a significant impact on existing solutions development and aftersales market. It has been more than three decades since the introduction of the first software component in cars, and since then a vast amount of different services has been introduced, creating an ecosystem of complex applications, architectures, and platforms. The complexity of the connected car ecosystem results into a range of new challenges. The backend applications must be scalable and flexible enough to accommodate loads created by the random user and device behavior. To deliver superior uptime, back-end systems must be highly integrated and automated to guarantee lowest possible failure rate, high availability, and fastest time-to-market. Connected car services increasingly rely on cloud-based service delivery models for improving user experiences and enhancing features for millions of vehicles and their users on a daily basis. Nowadays, the software applications become more complex, and the number of components that are involved and interact with each other is extremely large. In such systems, if a fault occurs, it can easily propagate and can affect other components resulting in a complex problem which is difficult to detect and debugg, therefore a robust and resilient architecture is needed which ensures the continuous availability of system in the wake of component failures, making the overall system highly available. The goal of the thesis is to gain insight into the development of highly available applications and to explore the area of fault tolerance. This thesis outlines different design patterns and describes the capabilities of fault tolerance libraries for Java platform, and design the most appropriate solution for developing a highly available application and evaluate the behavior with stress and load testing using Chaos Monkey methodologies.

Until now, car ownership has been a symbol of wealth and personal freedom. The high value of the car in society has been enforced by the powerful automotive industry with their well-funded marketing budgets. Currently, there are one billion cars worldwide, possibly increasing to 2,8 billion by 2050. However, the awareness of the negative consequences of car ownership on the environment, cities, and individuals in terms of reduced personal and financial freedom is increasing. The trend towards collaborative consumption involving activities like sharing and trading is leading to a shift from ownership to the access of goods and services. In this context, carsharing is receiving more and more attention and the number of users for B2C carsharing models is increasing exponentially. The least-developed business model with the biggest opportunities in terms of environmental benefits is peer-to-peer carsharing (P2P carsharing). Providers face daunting problems in reaching critical mass, due to a lack of consumer acceptance. Academic contributions on the topic are rare. The goal of this dissertation is to capture the acceptance factors, barriers and success factors for P2P carsharing. Additionally, the phenomenon is explored within the perspective of disruptive technologies, including the connected car and autonomous vehicles. A comprehensive literature review including collaborative consumption, carsharing, and in particular P2P carsharing, has been conducted. A mixed-method approach has been used. Qualitative interviews with leading academic and industry experts in the field of collaborative consumption and shared mobility, as well as a focus group discussion, have been executed. In the quantitative survey, the identified factors have been integrated into the Technological Acceptance Model (TAM), the theoretical foundation of the work. A representative survey was conducted in Austria with 801 respondents. The results were generated by applying a partial least squares analysis. Results show that the TAM model, including the extensions, appeared to be applicable. In particular, people with an innovative mindset are open to the usage of the business model. The main motivational factors for participating are economic, utility and enjoyment. The personal attachment towards one's own car remains one of the main barriers, next to fear of sharing and loss of convenience. Success factors in increasing acceptance are - among others - trust, value-added services and keyless car exchange. The preferred usage model for autonomous vehicles tends to be ownership. Even though the awareness of P2P carsharing is rather low among the Austrian population, 13,6% state that they would use the service. Sharing one's privately owned autonomous vehicle with others met with even higher levels of approval from the respondents. The extension of the TAM, as well as its application to a new field outside information system (IS) research, can be viewed as the major academic contribution of this work. Practical implications for P2P carsharing providers and the automotive industry include strategic recommendations regarding the current disruptive trends within the automotive industry. In particular, concrete measures have been identified to scale the business model by addressing new customers and reducing the identified barriers by providing extensive knowledge of relevant success factors.

The connected car segment has high demands on the exchange of data between the car on the road, and a variety of services in the backend. By the end of 2020, connected services will be mainstream automotive offerings, according to Telefónica - Connected Car Industry Report 2014 the overall number of vehicles with built-in internet connectivity will increase from 10% of the overall market today to 90% by the end of the decade [1]. Connected car solutions will soon become one of the major business drivers for the industry; they already have a significant impact on existing solutions development and aftersales market. It has been more than three decades since the introduction of the first software component in cars, and since then a vast amount of different services has been introduced, creating an ecosystem of complex applications, architectures, and platforms. The complexity of the connected car ecosystem results into a range of new challenges. The backend applications must be scalable and flexible enough to accommodate loads created by the random user and device behavior. To deliver superior uptime, back-end systems must be highly integrated and automated to guarantee lowest possible failure rate, high availability, and fastest time-to-market. Connected car services increasingly rely on cloud-based service delivery models for improving user experiences and enhancing features for millions of vehicles and their users on a daily basis. Nowadays, the software applications become more complex, and the number of components that are involved and interact with each other is extremely large. In such systems, if a fault occurs, it can easily propagate and can affect other components resulting in a complex problem which is difficult to detect and debugg, therefore a robust and resilient architecture is needed which ensures the continuous availability of system in the wake of component failures, making the overall system highly available. The goal of the thesis is to gain insight into the development of highly available applications and to explore the area of fault tolerance. This thesis outlines different design patterns and describes the capabilities of fault tolerance libraries for Java platform, and design the most appropriate solution for developing a highly available application and evaluate the behavior with stress and load testing using Chaos Monkey methodologies.

Récemment, les villes sont devenues "de plus en plus intelligentes", avec une multitude de périphériques IoT et de capteurs déployés partout. Parmi ces objets intelligents, les voitures peuvent jouer un rôle important. Les véhicules sont (ou seront), en effet, équipés avec plusieurs interfaces réseau, ils ont (ou auront) des capacités de calcul et des dispositifs capables d'analyser l'environnement. Pour réaliser le concept de "connected-car" il faut un changement de modèle Internet, à partir d'une architecture centrée sur l'hôte (IP) vers un paradigme centré sur l'information, comment l'architecture ICN (Information Centric Networking). Cette thèse analyse ainsi les avantages et les défis du paradigme ICN, en particulier du Named Data Networking (NDN), dans le domaine VANET, en présentant la première implémentation de NDN pour VANET (V-NDN). Il propose ensuite Navigo, un mécanisme de forwarding basé sur NDN pour la récupération de contenu en utilisant les communications V2V et V2I. Ensuite, le problème de la mobilité des fournisseurs de données est traité, proposant une solution distribuée basée sur NDN, MAP-Me. Toutefois, le rôle du véhicule dans les villes intelligentes ne s'arrête pas au niveau de la connectivité. Les voitures, avec leurs nouvelles capacités de calcul, sont les candidates idéales pour jouer un rôle dans l'architecture Fog Computing, en déplaçant des tâches de calcul vers l'edge du réseau. En tant que preuve de concept, cette thèse présente ParkMaster, un système qui combine les techniques de machine learning, le cloud et l'edge pour analyser l'environnement et traiter le problème de la disponibilité du stationnement. / In the recent years we have seen a continuous integration of technology with the urban environment. This fusion aims to improve the efficiency and the quality of living in big urban agglomerates, while reducing the costs for their management. Cities are getting “smarter and smarter”, with a plethora of IoT devices and sensors deployed all over the urban areas. Among those intelligent objects, an important role may be played by cars. Modern vehicles are (or will be) indeed equipped with multiple network interfaces, they have (or will have) computational capabilities and devices able to sense the environment. However, smart and connected cars do not represent only an opportunity, but also a challenge. Computation capabilities are limited, mobility and the diversity of network interfaces are obstacles when providing connectivity to the Internet and to other vehicles. When addressing the networking aspect, we believe that a shift in the Internet model is needed, from a host oriented architecture (IP) to a more content focused paradigm, the Information Centric Networking (ICN) architectures. This thesis thus analyzes the benefits and the challenges of the ICN paradigm, in particular of Named Data Networking (NDN), in the VANET domain, presenting the first implementation running on real cars of NDN for VANET (V-NDN). It then proposes Navigo, an NDN based forwarding mechanism for content retrieval over V2V and V2I communications, with the goal of efficiently discovering and retrieving data while reducing the network overhead. Networking mobility is not only a challenge for vehicles, but for any connected mobile device. For this reason, this thesis extends its initial area of interest — VANET — and addresses the network mobility problem for generic mobile nodes, proposing a NDN-based solution, dubbed MAP–Me. MAP-Me tackles the intra-AS content provider mobility problem without relying on any fixed node in the network. It exploits notifications messages at the time of a handover and the forwarding plane to maintain the data provider “always” reachable.Finally, the “connected car” concept is not the only novel element in modern vehicles. Cars indeed won’t be only connected, but also smart, able to locally process data produced by in-car sensors. Vehicles are the perfect candidates to play an important role in the recently proposed Fog Computing architecture. Such an architecture moves computational tasks typical of the cloud away from it and brings them to the edge, closer to where the data is produced. To prove that such a model, with the car as computing edge node, is already feasible with the current technology and not only a vision for the future, this thesis presents ParkMaster. Parkmaster is a fully deployed edge-based system that combines vision and machine learning techniques, the edge (driver’s smartphone) and the cloud to sense the environment and tackle the parking availability problem.

Die Bedeutung innovativer Geschäftsmodelle als Bestimmungsfaktor für den Unternehmenserfolg steht weitestgehend außer Frage. Aufgrund der hohen Komplexität von Geschäftsmodellen hat sich jedoch bislang kein praktisch anwendbares Bewertungskonzept etablieren können, welches Geschäftsmodellinnovationen in Hinblick auf deren Erfolgsentwicklung untersucht. Zur Adressierung dieser Problemstellung wird unter Anwendung des systemdynamischen Ansatzes ein Simulationsmodell entwickelt, welches den Wertbeitrag einer Geschäftsmodellinnovation ausweist. Neben dem Kapitalwert als finanzielle Wertgröße des Geschäftsmodells werden ferner der Kundenwert sowie der Wert der unternehmerischen Fähigkeiten als wichtige Wertgrößen explizit gemacht, da sie die zukünftige Leistungs- und Wettbewerbsfähigkeit des Geschäftsmodells determinieren. Damit liefert das Bewertungsmodell einen Ansatz zur ganzheitlichen Geschäftsmodellbewertung, die die Anwendung finanzieller Standardkalkulationen mit der Messbarmachung nicht-finanzieller Erfolgsgrößen kombiniert.:1 Einführung 2 Geschäftsmodelle und Geschäftsmodellbewertung 3 Entwicklungsprozess des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 4 Aufbau des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 5 Simulation des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 6 Schlussbetrachtung / Business model innovations provide powerful levers for creating sustainable competitive advantage and thus have a positive impact on the value of an enterprise. However, due to the complexity of business models, no practically applicable framework, evaluating an innovative business model with regard to its effect on a company’s success, has been established. Hence, a simulation model assessing the value contribution of a business model innovation is developed. Using the mathematical modeling technique ‘System Dynamics’ to frame the value drivers of a business allows for simulation experiments that reveal the effect of the business model’s design on its profitability, therewith guiding policymakers towards better decisions. As a result, the simulation model reports the net present value of a business model. In addition, the success indicators customer lifetime value and the value of the enterprises’ capabilities are identified as important assets that have to be monitored closely as they determine the company’s prospective performance. In combining financial standard calculations with the operationalization of non-financial measures, the simulation model represents a comprehensive approach for business model evaluation.:1 Einführung 2 Geschäftsmodelle und Geschäftsmodellbewertung 3 Entwicklungsprozess des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 4 Aufbau des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 5 Simulation des systemdynamischen Geschäftsmodells bedarfsabhängiger Funktionserweiterungen 6 Schlussbetrachtung

Dans le cadre du développement des innovations dans les Systèmes de Transport Intelligents, les véhicules connectés devront être capables de télécharger des informations basées sur la position sur et depuis des serveurs distants. Ces véhicules seront équipés avec des différentes technologies d’accès radio, telles que les réseaux cellulaires ou les réseaux véhicule-à-véhicule (V2V) comme IEEE 802.11p. Les réseaux cellulaires, avec une couverture presque omniprésente, fournissent un accès à internet avec garanties de qualité de service. Cependant, l’accès à ces réseaux est payant. Dans cette thèse, un algorithme de clustering multi-saut est proposé avec pour objectif de réduire le coût d’accès au réseau cellulaire en agrégeant des données sur le réseau V2V. Pour faire ceci, le leader du cluster (CH, de l’anglais Cluster Head) est utilisé comme passerelle unique vers le réseau cellulaire. Pour le test d’une application d’exemple pour télécharger du Floating Car Data agrégé, les résultats des simulations montrent que cette approche réduit l’utilisation du réseau cellulaire de plus de 80%, en s’attaquant à la redondance typique des données basées sur la position dans les réseaux véhiculaires. Il y a une contribution en trois parties : Premièrement, une approche pour déléguer la sélection du CH à la station de base du réseau cellulaire afin de maximiser la taille des clusters, et par conséquent le taux de compression. Deuxièmement, un algorithme auto-adaptatif qui change dynamiquement le nombre maximum de sauts afin de maintenir un équilibre entre la réduction des coûts d’accès au réseau cellulaire et le taux de perte de paquets dans le réseau V2V. Finalement, l’incorporation d’une théorie de la justice distributive, afin d’améliorer l’équité sur la durée concernant la distribution des coûts auxquels les CH doivent faire face, améliorant ainsi l’acceptabilité sociale de la proposition. Les algorithmes proposés ont été testés via simulation, et les résultats montrent une réduction significative dans l’utilisation du réseau cellulaire, une adaptation réussie du nombre de sauts aux changements de la densité du trafic véhiculaire, et une amélioration dans les métriques d’équité, sans affecter la performance des réseaux. / For the development of innovative Intelligent Transportation Systems applications, connected vehicles will frequently need to upload and download position-based information to and from servers. These vehicles will be equipped with different Radio Access Technologies (RAT), like cellular and vehicle-to-vehicle (V2V) technologies such as LTE and IEEE 802.11p respectively. Cellular networkscan provide internet access almost anywhere, with QoS guarantees. However, accessing these networks has an economic cost. In this thesis, a multi-hop clustering algorithm is proposed in the aim of reducing the cellular access costs by aggregating information and off-loading data in the V2V network, using the Cluster Head as a single gateway to the cellular network. For the example application of uploading aggregated Floating Car Data, simulation results show that this approach reduce cellular data consumption by more than 80% by reducing the typical redundancy of position-based data in a vehicular network. There is a threefold contribution: First, an approach that delegates the Cluster Head selection to the cellular base station in order to maximize the cluster size, thus maximizing aggregation. Secondly, a self-adaptation algorithm that dynamically changes the maximum number of hops, addressing the trade-off between cellular access reduction and V2V packet loss. Finally, the incorporation of a theory of distributive justice, for improving fairness over time regarding the distribution of the cost in which Cluster Heads have to incur, thus improving the proposal’s social acceptability. The proposed algorithms were tested via simulation, and the results show a significant reduction in cellular network usage, a successful adaptation of the number of hops to changes in the vehicular traffic density, and an improvement in fairness metrics, without affecting network performance.