Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Cooperative control of autonomous connected vehicles from a Networked Control perspective: Theory and experimental validation

Formation control of autonomous connected vehicles is one of the typical problems addressed in the general context of networked control systems. By leveraging this paradigm, a platoon composed by multiple connected and automated vehicles is represented as one-dimensional network of dynamical agents, in which each agent only uses its neighboring information to locally control its motion, while it aims to achieve certain global coordination with all other agents. Within this theoretical framework, control algorithms are traditionally designed based on an implicit assumption of unlimited bandwidth and perfect communication environments. However, in practice, wireless communication networks, enabling the cooperative driving applications, introduce unavoidable communication impairments such as transmission delay and packet losses that strongly affect the performances of cooperative driving. Moreover, in addition to this problem, wireless communication networks can suffer different security threats. The challenge in the control field is hence to design cooperative control algorithms that are robust to communication impairments and resilient to cyber attacks. The work aim is to tackle and solve these challenges by proposing different properly designed control strategies. They are validated both in analytical, numerical and experimental ways. Obtained results confirm the effectiveness of the strategies in coping with communication impairments and security vulnerabilities

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

Studies on Complex and Connected Vehicle Traffic Networks

Transportation networks such as road networks are well-known for their complexity. Its users make choices of route, which mode to take, etc.; these users then interact with each other, producing emergent dynamics such as traffic jams on roads. These localized multi-user emergent physical phenomena then interact with similar group movements occurring in other locations, creating more complex network-scale dynamics. These patterns of hierarchical levels of organization and emergent phenomena at each level are typical of so-called "complex systems." In addition, the increasing adoption of information-technology systems like connected and autonomous vehicles is creating new challenges in modeling transportation networks, as new emergent behaviors become possible, but also provide new sources of information and possibilities for traffic operations management.The complexity of transportation networks precludes the use of a single all-encompassing theory for all situations at all scales. This dissertation describes several analyses into understanding and controlling emergent dynamics on road traffic networks. It is broken into three parts. The first part proposes models for several new phenomena at the "macroscopic," group-of-vehicles to group-of-vehicles, level. In particular, we solve a problem of modeling arbitrary road junctions with populations of behaviorally-heterogenous vehicles, where the vehicle flows are modelled by a continuum-approximation, partial-differential-equation-based model. We also present several new modeling constructions for a particular complex road network topology: freeways with managed lanes. It has been noted that these managed lane-freeway networks induce new emergent behaviors that are not present in traditional freeways; we propose modeling techniques for several of them, and fit them into traditional modeling paradigms.The second part presents several contributions for estimating the state of the macro-scale traffic dynamics on the road network, based on the micro-scale data of global navigational satellite system readings of the speed and position of individual vehicles. These contributions are extensions of the particle filtering mathematical framework. First, we demonstrate the use of a Rao-Blackwellized particle filter in assimilating vehicle-local speed measurements to better estimate the macroscopic density state of a freeway. Then, we propose new "hypothesis-testing" particle filters that can be used to reject outlier or otherwise malign measurements in a principled statistical manner.The third and final part presents two items on applying deep neural networks to transportation system problems at smaller scales. Both items make use of neural attention, which is a neural network design technique that allows for the integration of structural domain knowledge. First, we demonstrate the applicability of this technique towards estimating aggregate traffic states at the lane level, and present evidence that designing the neural network architecture to encode different types of lane-to-lane relationships (e.g., upstream lane vs neighboring lane) greatly benefits statistical learning. Then, we apply similar methods to an autonomous vehicle coordination problem in a deep reinforcement learning framework, and show that an attention-based neural network that allows each vehicle to attend to the other vehicles enables superior learning compared to a naive, non-attention-based architecture, and also allows principled generalization between varying numbers of vehicles

Contribution to design a communication framework for vehicular ad hoc networks in urban scenarios

The constant mobility of people, the growing need to be always connected, the large number of vehicles that nowadays can be found in the roads and the advances in technology make Vehicular Ad hoc Networks (VANETs) be a major area of research. Vehicular Ad hoc Networks are a special type of wireless Mobile Ad hoc Networks (MANETs), which allow a group of mobile nodes configure a temporary network and maintain it without the need of a fixed infrastructure. A vehicular network presents some specific characteristics, as the very high speed of nodes. Due to this high speed the topology changes are frequent and the communication links may last only a few seconds. Smart cities are now a reality and have a direct relationship with vehicular networks. With the help of existing infrastructure such as traffic lights, we propose a scheme to update and analyse traffic density and a warning system to spread alert messages. With this, traffic lights assist vehicular networks to take proper decisions. This would ensure less congested streets. It would also be possible that the routing protocol forwards data packets to vehicles on streets with enough neighbours to increase the possibility of delivering the packets to destination. Sharing updated, reliable and real-time information, about traffic conditions, weather or security alerts, increases the need of algorithms for the dissemination of information that take into account the main beneffits and constraints of these networks. For all this, routing protocols for vehicular networks have the difficult task to select and establish transmission links to send the data packets from source to destination through multiple nodes using intermediate vehicles efficiently. The main objective of this thesis is to provide improvements in the communication framework for vehicular networks to improve decisions to select next hops in the moment to send information, in this way improving the exchange of information to provide suitable communication to minimize accidents, reduce congestion, optimize resources for emergencies, etc. Also, we include intelligence to vehicles at the moment to take routing decisions. Making them map-aware, being conscious of the presence of buildings and other obstacles in urban environments. Furthermore, our proposal considers the decision to store packets for a maximum time until finding other neighbouring nodes to forward the packets before discarding them. For this, we propose a protocol that considers multiple metrics that we call MMMR (A Multimetric, Map-Aware Routing Protocol ). MMMR is a protocol based on geographical knowledge of the environment and vehicle location. The metrics considered are the distance, the density of vehicles in transmission range, the available bandwidth and the future trajectory of the neighbouring nodes. This allows us to have a complete view of the vehicular scenario to anticipate the driver about possible changes that may occur. Thus, a node can select a node among all its neighbours, which is the best option to increase the likelihood of successful packet delivery, minimizing time and offering a level of quality and service. In the same way, being aware of the increase of information in wireless environments, we analyse the possibility of offering anonymity services. We include a mechanism of anonymity in routing protocols based on the Crowd algorithm, which uses the idea of hiding the original source of a packet. This allowed us to add some level of anonymity on VANET routing protocols. The analytical modeling of the available bandwidth between nodes in a VANET, the use of city infrastructure in a smart way, the forwarding selection in data routing byvehicles and the provision of anonymity in communications, are issues that have been addressed in this PhD thesis. In our research work we provide contributions to improve the communication framework for Vehicular Ad hoc Networks obtaining benefits toenhance the everyday of the population.La movilidad constante de las personas y la creciente necesidad de estar conectados en todo momento ha hecho de las redes vehiculares un área cuyo interés ha ido en aumento. La gran cantidad de vehículos que hay en la actualidad, y los avances tecnológicos han hecho de las redes vehiculares (VANETS, Vehicular Ad hoc Networks) un gran campo de investigación. Las redes vehiculares son un tipo especial de redes móviles ad hoc inalámbricas, las cuales, al igual que las redes MANET (Mobile Ad hoc Networks), permiten a un grupo de nodos móviles tanto configurar como mantener una red temporal por si mismos sin la necesidad de una infraestructura fija. Las redes vehiculares presentan algunas características muy representativas, por ejemplo, la alta velocidad que pueden alcanzar los nodos, en este caso vehículos. Debido a esta alta velocidad la topología cambia frecuentemente y la duración de los enlaces de comunicación puede ser de unos pocos segundos. Estas redes tienen una amplia área de aplicación, pudiendo tener comunicación entre los mismos nodos (V2V) o entre los vehículos y una infraestructura fija (V2I). Uno de los principales desafíos existentes en las VANET es la seguridad vial donde el gobierno y fabricantes de automóviles han centrado principalmente sus esfuerzos. Gracias a la rápida evolución de las tecnologías de comunicación inalámbrica los investigadores han logrado introducir las redes vehiculares dentro de las comunicaciones diarias permitiendo una amplia variedad de servicios para ofrecer. Las ciudades inteligentes son ahora una realidad y tienen una relación directa con las redes vehiculares. Con la ayuda de la infraestructura existente, como semáforos, se propone un sistema de análisis de densidad de tráfico y mensajes de alerta. Con esto, los semáforos ayudan a la red vehicular en la toma de decisiones. Así se logrará disponer de calles menos congestionadas para hacer una circulación más fluida (lo cual disminuye la contaminación). Además, sería posible que el protocolo de encaminamiento de datos elija vehículos en calles con suficientes vecinos para incrementar la posibilidad de entregar los paquetes al destino (minimizando pérdidas de información). El compartir información actualizada, confiable y en tiempo real sobre el estado del tráfico, clima o alertas de seguridad, aumenta la necesidad de algoritmos de difusión de la información que consideren los principales beneficios y restricciones de estas redes. Así mismo, considerar servicios críticos que necesiten un nivel de calidad y servicio es otro desafío importante. Por todo esto, un protocolo de encaminamiento para este tipo de redes tiene la difícil tarea de seleccionar y establecer enlaces de transmisión para enviar los datos desde el origen hacia el destino vía múltiples nodos utilizando vehículos intermedios de una manera eficiente. El principal objetivo de esta tesis es ofrecer mejoras en los sistemas de comunicación vehicular que mejoren la toma de decisiones en el momento de realizar el envío de la información, con lo cual se mejora el intercambio de información para poder ofrecer comunicación oportuna que minimice accidentes, reduzca atascos, optimice los recursos destinados a emergencias, etc. Así mismo, incluimos más inteligencia a los coches en el momento de tomar decisiones de encaminamiento de paquetes. Haciéndolos conscientes de la presencia de edificios y otros obstáculos en los entornos urbanos. Así como tomar la decisión de guardar paquetes durante un tiempo máximo de modo que se encuentre otros nodos vecinos para encaminar paquetes de información antes de descartarlo. Para esto, proponemos un protocolo basado en múltiples métricas (MMMR, A Multimetric, Map-aware Routing Protocol ) que es un protocolo geográfio basado en el conocimiento del entorno y localización de los vehículos. Las métricas consideradas son la distancia, la densidad de vehículos en el área de transmisión, el ancho de banda disponible y la trayectoria futura de los nodos vecinos. Esto nos permite tener una visión completa del escenario vehicular y anticiparnos a los posibles cambios que puedan suceder. Así, un nodo podrá seleccionar aquel nodo entre todos sus vecinos posibles que sea la mejor opción para incrementar la posibilidad de entrega exitosa de paquetes, minimizando tiempos y ofreciendo un cierto nivel de calidad y servicio. De la misma manera, conscientes del incremento de información que circula por medios inalámbricos, se analizó la posibilidad de servicios de anonimato. Incluimos pues un mecanismo de anonimato en protocolos de encaminamiento basado en el algoritmo Crowd, que se basa en la idea de ocultar la fuente original de un paquete. Esto nos permitió añadir cierto nivel de anonimato que pueden ofrecer los protocolos de encaminamiento. El modelado analítico del ancho de banda disponible entre nodos de una VANET, el uso de la infraestructura de la ciudad de una manera inteligente, la adecuada toma de decisiones de encaminamiento de datos por parte de los vehículos y la disposición de anonimato en las comunicaciones, son problemas que han sido abordados en este trabajo de tesis doctoral que ofrece contribuciones a la mejora de las comunicaciones en redes vehiculares en entornos urbanos aportando beneficios en el desarrollo de la vida diaria de la población