[pt] A crescente demanda por sistemas de transporte autônomos e inteligentes exige o desenvolvimento de técnicas avançadas de controle e estimativa, visando garantir operações seguras e eficientes. Devido à natureza não linear da dinâmica veicular e seus fenômenos característicos, os métodos clássicos de estimativa e controle podem não alcançar resultados adequados, o que incentiva a pesquisa de novos algoritmos. Por algumas contribuições, a primeira parte deste trabalho trata de algoritmos de estimação, tanto para identificação de parâmetros invariantes no tempo, quanto para estimação de estados e parâmetros variantes no tempo. Especial destaque é dados aos algoritmos de Estimação de Estados por Horizonte Móvel (MHSE), que se apresenta como robusto e preciso, devido ao problema de otimização com restrição em que se baseia. Este algoritmo é avaliado em dinâmica longitudinal de veículos, para estimativa de deslizamento longitudinal e coeficiente de atrito pneu-estrada. Apesar de sua eficiência, o alto custo computacional torna necessária a busca por alternativas sub-ótimas, e o emprego de Redes Neurais que mapeiam os resultados da otimização é uma solução promissora, que é tratada como Estimação por Horizonte Móvel com Redes Neurais (NNMHE). O NNMHE é avaliado em uma estimativa do estado de carga (SOC) de baterias para veículos elétricos, demonstrando, através de dados experimentais, que o NNMHE emula com precisão o problema de otimização e a literatura indica sua aplicação efetiva em hardwares embarcados. Por fim, é apresentada uma contribuição sobre o controle preditivo baseado em modelo não linear (NMPC). É proposto e avaliado seu uso compondo uma nova estrutura de controle hierárquica para veículos elétricos com motores independentes nas rodas, através do qual é possível controlar adequadamente o veículo em tarefas de rastreamento de velocidade e trajetória, com reduzido esforço computacional. O controle é avaliado usando dados experimentais de pneus obtidos, que aproximam a simulação de situações reais. / [en] The rising demand of autonomous and intelligent transportation systems requires the development of advanced control and estimation techniques, aiming to ensure safety and efficient operations. Due to the nonlinear nature of vehicle dynamics and its characteristic phenomena, classical estimation and control methods may not achieve adequate results, which encourages the research of novel algorithms. By some contributions, the first part of this work deals with estimation algorithms, both for identification of time invariant parameters and for estimation of states and time varying parameters. Special emphasis is given to Moving-Horizon State Estimation (MHSE), which is presented to be robust and accurate, due to the constrained optimization problem on which it is based. This algorithm is evaluated in vehicle longitudinal dynamics, for slip and tire-road friction estimation. Despite its efficiency, the high computational cost makes it necessary to search for suboptimal alternatives, and the employ of a Neural Networks that maps the optimization results is a promising solution, which is treated as Neural Networks Moving-Horizon Estimation (NNMHE). The NNMHE is evaluated on a state-of-charge (SOC) estimation of batteries for electric vehicles, demonstrating, through experimental data, that the NNMHE emulates accurately the optimization problem, and the literature indicates its effectively application on embedded hardware. Finally, a contribution about Nonlinear Model-based Predictive Control (NMPC) is presented. It is proposed and evaluated its use compounding a novel hierarchical control framework for electric vehicles with independent in-wheel motors, through which it is possible to adequately control the vehicle on velocity and path tracking tasks, with reduced computational effort. The control is evaluated using experimental obtained tire data, which approaches the simulation to real situations.

We address the question of performance of large-scale networks with distributed feedback control. We consider networked dynamical systems with single and double integrator dynamics, subject to distributed disturbances. We focus on two types of problems. First, we consider problems modeled over regular lattice structures. Here, we treat consensus and vehicular formation problems and evaluate performance in terms of measures of “global order”, which capture the notion of network coherence. Second, we consider electric power networks, which we treat as dynamical systems modeled over general graphs. Here, we evaluate performance in terms of the resistive power losses that are incurred in maintaining network synchrony. These losses are associated with transient power flows that are a consequence of “local disorder” caused by lack of synchrony. In both cases, we characterize fundamental limitations to performance as networks become large. Previous studies have shown that such limitations hold for coherence in networks with regular lattice structures. These imply that connections in 3 spatial dimensions are necessary to achieve full coherence, when the controller uses static feedback from relative measurements in a local neighborhood. We show that these limitations remain valid also with dynamic feedback, where each controller has an internal memory state. However, if the controller can access certain absolute state information, dynamic feedback can improve performance compared to static feedback, allowing also 1-dimensional formations to be fully coherent. For electric power networks, we show that the transient power losses grow unboundedly with network size. However, in contrast to previous results, performance does not improve with increased network connectivity. We also show that a certain type of distributed dynamic feedback controller can improve performance by reducing losses, but that their scaling with network size remains an important limitation. / <p>QC 20160504</p>

The Vehicular Ad Hoc Network (VANET) is a promising approach for future Intelligent Transportation Systems (ITS) implementation. The data transmission is wireless primarily in the VANET system. The secure data transmission in VANET attracts research attention without any doubt. The Ciphertext-Policy Attribute-Based Encryption (CP-ABE) provides an encrypted access control mechanism for broadcasting messages in VANET. The user’s attributes stand for its current property. However, if we apply vehicle location as the attribute, this attribute has to keep up-to-date with the vehicle’s movement. It is not easy for current CP-ABE algorithms because whenever one attribute changes, the entire private key, which is based on all the attributes, must be changed. In this thesis, we apply fading function to realize the “dynamic attribute” feature in CP-ABE. The dynamic attribute allows the user to update each attribute separately, and fading function gives each attribute a valid period. We introduce the dynamic attribute feature to three different CP-ABE algorithms. Then we design a VANET system that applies the CP-ABE with dynamic attribute feature. We evaluate the processing time of three different CP-ABE algorithms. We apply two different pairing curves for different security requirements. Our results show that the introduction of fading function does not cause significant extra time cost to current CP-ABE algorithms. The fading function causes extra 0.2ms on average for each attribute that participates in encryption and decryption. The sum-up time for encryption and decryption is between 100ms to 200ms when there are ten attributes participating in encryption and decryption. / VANET är ett lovande tillvägagångssätt för framtida genomförande av ITS. Dataöverföringen är i första hand trådlös i VANET-systemet. Den säkra dataöverföringen i VANET är utan tvekan föremål för forskningens uppmärksamhet. CP-ABE ger en krypterad åtkomstkontrollmekanism för sändning av meddelanden i VANET. Användarens attribut står för dennes aktuella egenskaper. Men om vi använder fordonets position som attribut måste detta attribut hålla sig uppdaterat med fordonets rörelse. Det är inte lätt för de nuvarande CP-ABE-algoritmerna eftersom hela den privata nyckeln, som är baserad på alla attribut, måste ändras när ett attribut ändras. I den här avhandlingen tillämpar vi fading-funktionen för att realisera funktionen ”dynamiskt attribut” i CP-ABE. Det dynamiska attributet gör det möjligt för användaren att uppdatera varje attribut separat, och fading-funktionen ger varje attribut en giltighetstid. Vi inför den dynamiska attributfunktionen i tre olika CP-ABE-algoritmer. Därefter utformar vi ett VANET-system som tillämpar CP-ABE med dynamisk attributfunktion. Vi utvärderar tidsåtgången för tre olika CP-ABE-algoritmer. Vi tillämpar två olika parningskurvor för olika säkerhetskrav. Våra resultat visar att införandet av fading-funktionen inte orsakar någon betydande tidsåtgång för de nuvarande CP-ABE-algoritmerna. Fading-funktionen orsakar i genomsnitt 0,2 ms extra för varje attribut som deltar i kryptering och dekryptering. Den sammanlagda tiden för kryptering och dekryptering är mellan 100 och 200 ms när tio attribut deltar i kryptering och dekryptering.

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Connected and autonomous vehicles are emerging technologies that have fostered the Intelligent Transportation System (ITS). ITS has the objective of optimizing traffic safety, mobility, and fuel consumption. To achieve this, a range of different services are provided that utilize communication in a vehicular network. One of these services that has received a lot of attention lately due to its ongoing standardization is the Maneuver Coordination Service (MCS). MCS has already shown great potential in the support of complex traffic areas, also called Transition Area (TA), where vehicles must cooperate to avoid Transition of Controls (ToCs). ITS-services often rely on communicated data; small errors, such as inaccessible or incorrect data, can cause the system to behave incorrectly. Signal interference (jamming) can cause communication interruptions, making vehicles unaware of each other. Incorrect data can be intentional due to data injection attacks, but also unintentional due to malfunctioning sensors, making vehicles incorrectly aware of each other. Incorrect behavior in systems such as ITS can lead to traffic congestion or even life-threatening collisions. This study focuses on MCS and examines traffic behavior when the service, in a generic traffic scenario, is subjected to jamming and falsification attacks with a variety of strategies (negative and positive speed, acceleration and position offset). We considered external attackers (not authenticated) that can disrupt communication, as well as internal attackers (authenticated) that are limited to tampering with outgoing data. Through severe collisions and travel time delays, the results show an impact on both safety and mobility. The results also show that different attacks with different impacts on the adversary can cause similar effects on the traffic, thus allowing the adversary to choose attacks based on the desired impact and its rationality, i.e. its willingness to be part of the impact. The study also proposes an extension to an already proposed Maneuver Coordination Protocol (MCP). We show that our extended MCP can be beneficial in avoiding dangerous maneuvers that could lead to collisions with cars in the blind spot. / Uppkopplade och autonoma fordon är framväxande teknologier som har främjat Intelligenta Transporteringssystem (ITS). ITS har som mål att optimera trafiksäkerhet, mobilitet och bränsleförbrukning. För att uppnå detta tillhandahålls en rad olika tjänster som utnyttjar kommunikation i ett fordonsnät. En av dessa tjänster som har fått mycket uppmärksamhet under den senaste tiden, tack vare sin pågående standardisering, är Manöverkoordinationtjänsten (MCS). MCS har redan visat stor potential för att stödja komplexa trafikområden, även kallade Övergångsområden (TA), där fordon måste samarbeta för att undvika kontrollövergångar (ToCs). ITS-tjänster förlitar sig ofta på kommunicerad data; små fel, som otillgängliga eller felaktiga data, kan göra att systemet beter sig felaktigt. Signalstörningar kan orsaka kommunikationsavbrott, vilket gör fordon omedvetna om varandra. Felaktig data kan vara avsiktliga på grund av datainjektionsattacker, men också oavsiktliga på grund av felaktiga sensorer, vilket gör fordon felaktigt medvetna om varandra. Felaktigt beteende i system som ITS kan leda till trafikstockningar eller till och med livshotande kollisioner. Denna studie fokuserar på MCS och undersöker trafikbeteendet när tjänsten, i ett generiskt trafikscenario, utsätts för signalstörningar och förfalskningsattacker med en mängd olika strategier (negativ och positiv hastighet, acceleration och positionsförskjutning). Vi tog hänsyn till externa angripare (ej autentiserade) som kan störa kommunikationen, såväl som interna angripare (autentiserade) som är begränsade till att manipulera utgående data. Genom allvarliga kollisioner och restidsförseningar visar resultaten en inverkan på både säkerhet och mobilitet. Resultaten visar också att olika attacker med olika inverkan på angriparen kan orsaka liknande effekter på trafiken, vilket gör att angriparen kan välja attacker baserat på den önskade effekten och rationaliteten, d.v.s. dens villighet att vara en del av påverkan. Studien föreslår också en utökning av en redan föreslagen MCP. Vi visar att vårt utökade MCP kan vara till nytta för att undvika farliga manövrar som kan leda till kollisioner med bilar i döda vinkeln.

The Domain Name System is a hierarchical naming system that provides information of network resources or services given domain names. DNS applications in vehicular networks raise new challenges with regards to security and privacy of vehicles. In particular, vehicular communications outside the coverage of roadside infrastructure needs to be preserved. Multicast DNS is proposed as a method to restrict queries to vehicles in a Vehicle-to-Everything environment which could include other connected devices. Contemporary DNS applications rely on robust security protocols provided by the DNS Security Extensions to authenticate responses and verify resource records. Vehicular DNS communications need authentication to verify the source and legitimacy of DNS resource records. This can be achieved through multihop Vehicle- to-Vehicle communications to reach a name server supplemented by a novel approach to verify records using the Bloom filter.In this thesis, we analyze the security and privacy risks posed by a non-authenticated baseline communication protocol. We then build a secure and privacy-preserving networked system based on pseudonym certificate-based public key infrastructure solution. The experimental analysis confirmed the improvement on security and privacy at the cost of communication and computation overhead. / Domännamnssystemet är en hierarkisk benämningssystem som ger information om nätverksresurser eller tjänster för givna domännamn. DNS application i fordon nätverk framkallar nya utmaningar när det handlar om datasäkerhet och fordons integritet. Det är särskilt fordon kommunikation utanför vägkant-infrastrukturens räckvidd som behöver bevara och försäkra operationer av DNS applikation i fordon nätverk. Multicast DNS är en föreslagen metod för att begränsa förfrågan till fordon i en fordon-till-all-miljö som kan inkludera andra anslutna enheter. Nuvarande applikationer förlitar sig på en robust säkerhetsprotokoll som kommer från DNS säkerhetsförlängning för att autentisera svar och verifiera resurs rekord. Fordon DNS kommunikationer behöver autentisering för att verifiera källor och legitimitet av DNS resurs rekord. Detta kan uppnås genom multihop fordon-till-fordon kommunikation för att ansluta sig till en namn server med hjälp av en ny metod för att verifiera uppgifter med hjälp av bloomfilter datastruktur.I tesen analyserar vi risken som finns i en icke-autentiserad integritets-läckande kommunikationsprotokoll. Vi bygger sedan ett nätverk och använder en pseudonym certifikatbaserad publik nyckel infrastruktur lösning för att undersöka förbättringar inom säkerhet och integritet. Analysen från experimenten visar att det finns en förbättring för säkerhet och integritet i utbyte mot tidsprestanda, vilket är en intressant kompromiss.

[pt] Os veículos automatizados devem trafegar em determinado ambiente detectando, planejando e seguindo uma trajetória segura. De modo a se mostrarem mais seguros que seres humanos, eles devem ser capazes de executar essas tarefas tão bem ou melhor do que motoristas humanos sob diferentes condições críticas. Uma parte essencial no estudo de veículos automatizados o desenvolvimento de modelos representativos que sejam precisos e computacionalmente eficientes. Assim, para lidar com esses problemas, o presente trabalho aplica métodos de inteligência computational e identificação de sistemas para realizar modelagem de veículos e controle de rastreamento de trajetória. Primeiro, arquiteturas neurais são usadas para capturar as características do pneu na interação entre a dinâmica lateral e longitudinal do veículo, reduzindo o custo computacional em controladores preditivos. Em segundo lugar, uma combinação de modelos caixa-preta é usada para melhorar o controle preditivo. Em seguida, uma abordagem híbrida combina modelos baseados na física e orientados por dados com modelagem de caixa-preta das discrepâncias. Essa abordagem é escolhida para melhorar a precisão da modelagem de veículos, propondo um modelo de discrepância para capturar incompatibilidades entre modelos de veículos e dados medidos. Os resultados são mostrados quando os métodos propostos são aplicados a sistemas com dados simulados/reais e comparados com abordagens encontradas na literatura, mostrando um aumento de precisão (até 40 por cento) em termos de métricas baseadas em erro, com menor esforço computacional (redução de até 88 por cento) do que os controladores preditivos convencionais. / [en] Automated vehicles must travel in a given environment detecting, planning, and following a safe path. In order to be safer than humans, they must be able to perform these tasks as well or better than human drivers under different critical conditions. An essential part of the study of automated vehicles is the development of representative models that are accurate and computationally efficient. Thus, to cope with these problems, the present work applies artificial neural networks and system identification methods to perform vehicle modeling and trajectory tracking control. First, neural architectures are used to capture tire characteristics present in the interaction between lateral and longitudinal vehicle dynamics, reducing computational costs for predictive controllers. Secondly, a combination of black-box models is used to improve predictive control. Then, a hybrid approach combines physics-based and data-driven models with black-box modeling of the discrepancies. This approach is chosen to improve the accuracy of vehicle modeling by proposing a discrepancy model to capture mismatches between vehicle models and measured data. Results are shown when the proposed methods are applied to systems with simulated/real data and compared with approaches found in the literature, showing an increase of accuracy (up to 40 percent) in terms of error-based metrics while having lesser computational effort (reduction by up to 88 percent) than conventional predictive controllers.

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