New cooperative intelligent transportation system (C-ITS) applications become enabled thanks to advances in communication technologies between vehicles(V2V) and with the infrastructure (V2I). Communicating vehicles share information with each other and cooperate, which results in improved safety, fuel economy, and traffic efficiency. An example of a C-ITS application is platooning, which comprises a string of vehicles that travel together with short inter-vehicle distances (IVDs). Any solution related to C-ITS must comply with high safety requirements in order to pass standardization and be commercially deployed. Furthermore, trusted safety levels should be assured even for critical scenarios. This thesis studies the conditions that guarantee safety in emergency braking scenarios for heterogeneous platooning, or string-like, formations of vehicles. In such scenarios, the vehicle at the head of the string emergency brakes and all following vehicles have to automatically react in time to avoid rear-end collisions. The reaction time can be significantly decreased with vehicle-to-vehicle (V2V) communication usage since the leader can explicitly inform other platooning members about the critical braking. The safety analysis conducted in the thesis yields computationally efficient methods and algorithms for calculating minimum inter-vehicle distances that allow avoiding rear-end collisions with a predefined high guarantee. These IVDs are theoretically obtained for an open-loop and a closed-loop configurations. The former implies that follower drives with a constant velocity until braking starts, whereas in the latter, an adaptive cruise control (ACC) with a constant-distance policy serves as a controller. In addition, further optimization of inter-vehicle distances in the platoon is carried out under an assumption of centralized control. Such an approach allows achieving better fuel consumption and road utilization. The performed analytical comparison suggests that our proposed V2V communication based solution is superior to classical automated systems, such as automatic emergency braking system (AEBS), which utilizes only onboard sensors and no communication. Wireless communication, enabling to know the intentions of other vehicles almost immediately, allows for smaller IVDs whilst guaranteeing the same level of safety. Overall, the presented thesis highlights the importance of C-ITS and, specifically, V2V in the prevention of rear-end collisions in emergency scenarios. Future work directions include an extension of the obtained results by considering more advanced models of vehicles, environment, and communication settings; and applying the proposed algorithms of safety guaranteeing to other controllers, such as ACC with a constant time headway policy.

This thesis presents a novel approach to several problems in intelligent transportation systems using collaborative driving coordination. With inter-vehicle communication and intelligent vehicle cooperation, important tasks in transportation such as lane position determination, lane assignment and platoon formation can be solved. Several topics in regard to inter-vehicle communication, lane positioning, lane assignment and platoon formation are explored in this thesis: First, the design and experimental results of low-cost lane-level positioning system that can support a large number of transportation applications are discussed. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within the communication range of each other, the lane positions of vehicles can be determined. The robustness effectiveness of the system is shown in both simulations and real road tests. Second, a decentralized approach to lane scheduling for vehicles with an aim to increase traffic throughput while ensuring the vehicles exit successfully at their destinations is presented. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The lane optimization problem is formulated as a linear programming problem that can be solved using the Simplex method. Finally, a direction for cooperative vehicle platoon formation is proposed. To enhance traffic safety, increase lane capacities and reduce fuel consumption, vehicles can be organized into platoons with the objective of maximizing the travel distance that platoons stay intact. Toward this end, this work evaluates a proposed strategy which assigns vehicles to platoons by solving an optimization problem. A linear model for assigning vehicles to appropriate platoons when they enter the highway is formulated. Simulation results demonstrate that lane capacity can be increased effectively when platooning operation is used.

This thesis presents a novel approach to several problems in intelligent transportation systems using collaborative driving coordination. With inter-vehicle communication and intelligent vehicle cooperation, important tasks in transportation such as lane position determination, lane assignment and platoon formation can be solved. Several topics in regard to inter-vehicle communication, lane positioning, lane assignment and platoon formation are explored in this thesis: First, the design and experimental results of low-cost lane-level positioning system that can support a large number of transportation applications are discussed. Using a Markov-based approach based on sharing information among a group of vehicles that are traveling within the communication range of each other, the lane positions of vehicles can be determined. The robustness effectiveness of the system is shown in both simulations and real road tests. Second, a decentralized approach to lane scheduling for vehicles with an aim to increase traffic throughput while ensuring the vehicles exit successfully at their destinations is presented. Most of current traffic management systems do not consider lane organization of vehicles and only regulate traffic flows by controlling traffic signals or ramp meters. However, traffic throughput and efficient use of highways can be increased by coordinating driver behaviors intelligently. The lane optimization problem is formulated as a linear programming problem that can be solved using the Simplex method. Finally, a direction for cooperative vehicle platoon formation is proposed. To enhance traffic safety, increase lane capacities and reduce fuel consumption, vehicles can be organized into platoons with the objective of maximizing the travel distance that platoons stay intact. Toward this end, this work evaluates a proposed strategy which assigns vehicles to platoons by solving an optimization problem. A linear model for assigning vehicles to appropriate platoons when they enter the highway is formulated. Simulation results demonstrate that lane capacity can be increased effectively when platooning operation is used.

Advances in computing and wireless communication technologies have increased interest in smart vehicles, vehicles equipped with significant computing, communication and sensing capabilities to provide services to travelers. Smart vehicles can be exploited to improve driving safety and comfort as well as optimize surface transportation systems. Wireless communications among vehicles and between vehicles and roadside infrastructures represent an important class of vehicle communications. One can envision creating an integrated radio network leveraging various wireless technologies that work together in a seamless fashion. Based on cost-performance tradeoffs, different network configurations may be appropriate for different environments. An understanding of the properties of different vehicular network architectures is absolutely necessary before services can be successfully deployed. Based on this understanding, efficient data services (e.g., data dissemination services) can be designed to accommodate application requirements. This thesis examines several research topics concerning both the evaluation and design of vehicular networks. We explore the properties of vehicle-to-vehicle (v2v) communications. We study the spatial propagation of information along the road using v2v communications. Our analysis identifies the vehicle traffic characteristics that significantly affect information propagation. We also evaluate the feasibility of propagating information along a highway. Several design alternatives exist to build infrastructure-based vehicular networks. Their characteristics have been evaluated in a realistic vehicular environment. Based on these evaluations, we have developed some insights into the design of future broadband vehicular networks capable of adapting to varying vehicle traffic conditions. Based on the above analysis, opportunistic forwarding that exploit vehicle mobility to overcome vehicular network partitioning appears to be a viable approach for data dissemination using v2v communications for applications that can tolerate some data loss and delay. We introduce a methodology to design enhanced opportunistic forwarding algorithms. Practical algorithms derived from this methodology have exhibited different performance/overhead tradeoffs. An in-depth understanding of wireless communication performance in a vehicular environment is necessary to provide the groundwork for realizing reliable mobile communication services. We have conducted an extensive set of field experiments to uncover the performance of short-range communications between vehicles and between vehicles and roadside stations in a specific highway scenario.

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.

This dissertation explores the integration and optimization of advanced communication technologies within the collaborative mobile system (CMS), focusing on the system design, implementation, and evaluation over unmanned aerial vehicles (UAVs). Collectively, this dissertation tackles the key challenges of connectivity and performance within CMS. This work demonstrates practical implementations and sheds light on the challenges and opportunities for CMS. The dissertation emphasizes the importance of adaptability and scalability in network design and implementation, particularly in leveraging the integration of hardware and software to adapt to promising architectures. By providing insights into performance under real-world conditions, this work explores the interplay of innovations in UAVs, mobile communications, network architecture, and system performance, paving the way for future network investigation and development.

Dans la première partie de cette thèse, on formule deux méthodes pour le calcul d'une décomposition polyadique canonique avec facteurs matriciels linéairement structurés (tels que des facteurs de Toeplitz ou en bande): un algorithme de moindres carrés alternés contraint (CALS) et une solution algébrique dans le cas où tous les facteurs sont circulants. Des versions exacte et approchée de la première méthode sont étudiées. La deuxième méthode fait appel à la transformée de Fourier multidimensionnelle du tenseur considéré, ce qui conduit à la résolution d'un système d'équations monomiales homogènes. Nos simulations montrent que la combinaison de ces approches fournit un estimateur statistiquement efficace, ce qui reste vrai pour d'autres combinaisons de CALS dans des scénarios impliquant des facteurs non-circulants. La seconde partie de la thèse porte sur la récupération de tenseurs de rang faible et, en particulier, sur le problème de reconstruction tensorielle (TC). On propose un algorithme efficace, noté SeMPIHT, qui emploie des projections séquentiellement optimales par mode comme opérateur de seuillage dur. Une borne de performance est dérivée sous des conditions d'isométrie restreinte habituelles, ce qui fournit des bornes d'échantillonnage sous-optimales. Cependant, nos simulations suggèrent que SeMPIHT obéit à des bornes optimales pour des mesures Gaussiennes. Des heuristiques de sélection du pas et d'augmentation graduelle du rang sont aussi élaborées dans le but d'améliorer sa performance. On propose aussi un schéma d'imputation pour TC basé sur un seuillage doux du coeur du modèle de Tucker et son utilité est illustrée avec des données réelles de trafic routier / In the first part of this thesis, we formulate two methods for computing a canonical polyadic decomposition having linearly structured matrix factors (such as, e.g., Toeplitz or banded factors): a general constrained alternating least squares (CALS) algorithm and an algebraic solution for the case where all factors are circulant. Exact and approximate versions of the former method are studied. The latter method relies on a multidimensional discrete-time Fourier transform of the target tensor, which leads to a system of homogeneous monomial equations whose resolution provides the desired circulant factors. Our simulations show that combining these approaches yields a statistically efficient estimator, which is also true for other combinations of CALS in scenarios involving non-circulant factors. The second part of the thesis concerns low-rank tensor recovery (LRTR) and, in particular, the tensor completion (TC) problem. We propose an efficient algorithm, called SeMPIHT, employing sequentially optimal modal projections as its hard thresholding operator. Then, a performance bound is derived under usual restricted isometry conditions, which however yield suboptimal sampling bounds. Yet, our simulations suggest SeMPIHT obeys optimal sampling bounds for Gaussian measurements. Step size selection and gradual rank increase heuristics are also elaborated in order to improve performance. We also devise an imputation scheme for TC based on soft thresholding of a Tucker model core and illustrate its utility in completing real-world road traffic data acquired by an intelligent transportation

[ES] En la actualidad, uno de los principales desafíos a los que se enfrentan las grandes áreas metropolitanas es la congestión provocada por el tráfico, la cual se ha convertido en un problema importante al que se enfrentan las autoridades de cada ciudad. Para abordar este problema es necesario implementar una solución eficiente para controlar el tráfico que genere beneficios para los ciudadanos, como reducir los tiempos de viaje de los vehículos y, en consecuencia, el consumo de combustible, el ruido, y la contaminación ambiental. De hecho, al analizar adecuadamente la demanda de tráfico, es posible predecir las condiciones futuras del tráfico, y utilizar esa información para la optimización de las rutas tomadas por los vehículos. Este enfoque puede ser especialmente efectivo si se aplica en el contexto de los vehículos autónomos, que tienen un comportamiento más predecible, lo cual permite a los administradores de la ciudad mitigar los efectos de la congestión, como es la contaminación, al mejorar el flujo de tráfico de manera totalmente centralizada. La validación de este enfoque generalmente requiere el uso de simulaciones que deberían ser lo más realistas posible. Sin embargo, lograr altos grados de realismo puede ser complejo cuando los patrones de tráfico reales, definidos a través de una matriz de Origen/Destino (O-D) para los vehículos en una ciudad, son desconocidos, como ocurre la mayoría de las veces. Por lo tanto, la primera contribución de esta tesis es desarrollar una heurística iterativa para mejorar el modelado de la congestión de tráfico; a partir de las mediciones de bucle de inducción reales hechas por el Ayuntamiento de Valencia (España), pudimos generar una matriz O-D para la simulación de tráfico que se asemeja a la distribución de tráfico real. Si fuera posible caracterizar el estado del tráfico prediciendo las condiciones futuras del tráfico para optimizar la ruta de los vehículos automatizados, y si se pudieran tomar estas medidas para mitigar de manera preventiva los efectos de la congestión con sus problemas relacionados, se podría mejorar el flujo de tráfico en general. Por lo tanto, la segunda contribución de esta tesis es desarrollar una Ecuación de Predicción de Tráfico para caracterizar el comportamiento en las diferentes calles de la ciudad en términos de tiempo de viaje con respecto al volumen de tráfico, y aplicar una regresión logística a esos datos para predecir las condiciones futuras del tráfico. La tercera y última contribución de esta tesis apunta directamente al nuevo paradigma de gestión de tráfico previsto, tratándose de un servidor de rutas capaz de manejar todo el tráfico en una ciudad, y equilibrar los flujos de tráfico teniendo en cuenta las condiciones de congestión del tráfico presentes y futuras. Por lo tanto, realizamos un estudio de simulación con datos reales de congestión de tráfico en la ciudad de Valencia (España), para demostrar cómo se puede mejorar el flujo de tráfico en un día típico mediante la solución propuesta. Los resultados experimentales muestran que nuestra solución, combinada con una actualización frecuente de las condiciones del tráfico en el servidor de rutas, es capaz de lograr mejoras sustanciales en términos de velocidad promedio y tiempo de trayecto, ambos indicadores de un menor grado de congestión y de una mejor fluidez del tráfico. / [CA] En l'actualitat, un dels principals desafiaments als quals s'enfronten les grans àrees metropolitanes és la congestió provocada pel trànsit, que s'ha convertit en un problema important al qual s'enfronten les autoritats de cada ciutat. Per a abordar aquest problema és necessari implementar una solució eficient per a controlar el trànsit que genere beneficis per als ciutadans, com reduir els temps de viatge dels vehicles i, en conseqüència, el consum de combustible, el soroll, i la contaminació ambiental. De fet, en analitzar adequadament la demanda de trànsit, és possible predir les condicions futures del trànsit, i utilitzar aqueixa informació per a l'optimització de les rutes preses pels vehicles. Aquest enfocament pot ser especialment efectiu si s'aplica en el context dels vehicles autònoms, que tenen un comportament més predictible, i això permet als administradors de la ciutat mitigar els efectes de la congestió, com és la contaminació, en millorar el flux de trànsit de manera totalment centralitzada. La validació d'aquest enfocament generalment requereix l'ús de simulacions que haurien de ser el més realistes possible. No obstant això, aconseguir alts graus de realisme pot ser complex quan els patrons de trànsit reals, definits a través d'una matriu d'Origen/Destinació (O-D) per als vehicles en una ciutat, són desconeguts, com ocorre la majoria de les vegades. Per tant, la primera contribució d'aquesta tesi és desenvolupar una heurística iterativa per a millorar el modelatge de la congestió de trànsit; a partir dels mesuraments de bucle d'inducció reals fetes per l'Ajuntament de València (Espanya), vam poder generar una matriu O-D per a la simulació de trànsit que s'assembla a la distribució de trànsit real. Si fóra possible caracteritzar l'estat del trànsit predient les condicions futures del trànsit per a optimitzar la ruta dels vehicles automatitzats, i si es pogueren prendre aquestes mesures per a mitigar de manera preventiva els efectes de la congestió amb els seus problemes relacionats, es podria millorar el flux de trànsit en general. Per tant, la segona contribució d'aquesta tesi és desenvolupar una Equació de Predicció de Trànsit per a caracteritzar el comportament en els diferents carrers de la ciutat en termes de temps de viatge respecte al volum de trànsit, i aplicar una regressió logística a aqueixes dades per a predir les condicions futures del trànsit. La tercera i última contribució d'aquesta tesi apunta directament al nou paradigma de gestió de trànsit previst. Es tracta d'un servidor de rutes capaç de manejar tot el trànsit en una ciutat, i equilibrar els fluxos de trànsit tenint en compte les condicions de congestió del trànsit presents i futures. Per tant, realitzem un estudi de simulació amb dades reals de congestió de trànsit a la ciutat de València (Espanya), per a demostrar com es pot millorar el flux de trànsit en un dia típic mitjançant la solució proposada. Els resultats experimentals mostren que la nostra solució, combinada amb una actualització freqüent de les condicions del trànsit en el servidor de rutes, és capaç d'aconseguir millores substancials en termes de velocitat faig una mitjana i de temps de trajecte, tots dos indicadors d'un grau menor de congestió i d'una fluïdesa millor del trànsit. / [EN] Currently, one of the main challenges that large metropolitan areas have to face is traffic congestion, which has become an important problem faced by city authorities. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, use of fuel, noise and environmental pollution. In fact, by properly analyzing traffic demand, it becomes possible to predict future traffic conditions, and to use that information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution by improving the traffic flow in a city in a fully centralized manner. Validating this approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, are unknown, as occurs most of the times. Thus, the first contribution of this thesis is to develop an iterative heuristic for improving traffic congestion modeling; starting from real induction loop measurements made available by the City Hall of Valencia, Spain, we were able to generate an O-D matrix for traffic simulation that resembles the real traffic distribution. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. Thereby, the second contribution of this thesis was to develop a Traffic Prediction Equation to characterize the different streets of a city in terms of travel time with respect to the vehicle load, and applying logistic regression to those data to predict future traffic conditions. The third and last contribution of this thesis towards our envisioned traffic management paradigm was a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. Thus, we perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed solution, combined with frequent updating of traffic conditions on the route server, is able to achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity. / Finally, I want to thank the Ecuatorian Republic through the "Secretaría de Educación Superior, Ciencia, Tecnología e Innovación" (SENESCYT), for granting me the scholarship to finance my studies. / Zambrano Martínez, JL. (2019). Efficient Traffic Management in Urban Environments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/129865