Intelligent Traffic Control with Connected and Automated Vehicles

The recent advancements in communication technology, transportation infrastructure, computational techniques, and artificial intelligence are driving a revolution in future transportation systems. Connected and Automated Vehicles (CAVs) are attracting a lot of attention due to their potential to reduce traffic accidents, ease congestion, and improve traffic efficiency. This study focuses on addressing the challenges in controlling future CAV-enabled transportation systems. The aim is to develop a framework for the control of CAV-based traffic systems to improve roadway safety, travel efficiency, and energy efficiency. The study proposes new methods for vehicle speed control and traffic signal control at signalized intersections and corridors as well as merging roadways, to increase the understanding of how traffic elements interact and are impacted by individual actors. The vehicle speed control method is based on sequential convex programming (SCP) algorithms, combining the pseudospectral collocation method with line-search and trust-region techniques for optimal solutions with real-time performance and efficient handling of multiple constraints. In terms of on-ramp merging control, the study develops a new merging control approach that balances computational efficiency, solution optimality, and real-time performance for safe merging operations. The traffic signal control framework uses deep reinforcement learning (DRL) with a novel convolutional autoencoder network for a concise representation of traffic information to improve the learning efficiency of the DRL algorithm. The proposed method extends the action space by including both phase duration and cycle length, allowing for more adaptability to dynamic traffic flow. This study presents a comprehensive framework for the control of CAV-based traffic system that enhances the positive attributes of CAV technology while minimizing negative effects. The framework will contribute to improving road safety, travel, and energy efficiency while synchronizing CAV motion planning with traffic signal optimization to reduce traffic congestion and idling as well as fuel consumption with guaranteed collision avoidance. This study explores the interface of multiple disciplines including control theory, optimization, machine learning, data analytics, and real-time computation. The results of this study will inform future research in the area of intelligent control of data-rich, interactive systems and will benefit the development of intelligent transportation systems with CAV technologies

Trajectory planning based on collocation methods for multiple aerial and ground autonomous vehicles

Esta tesis doctorar presenta una serie de contribuciones en los métodos de coordinación y generación de trayectorias de grupos de vehículos, concretamente de vehículos autónomos. Los métodos de colocación, más conocidos por su nombre en inglés “Collocation methods”, han despertado un creciente interés en los últimos años, entre los distintos métodos numéricos para resolver cualquier tipo de problema dentro del campo de la ingeniería. Esta tesis en concreto, presenta un nuevo punto de vista dentro de los métodos de generación de trayectorias, gracias al uso de los métodos de colocación. El interés sobre los vehículos autónomos se ha visto intensificado en los últimos años. Gracias a la evolución de los sensores, la obtención de información del medio que rodea a un vehículo es cada vez más sencilla y fiable. Esto permite a los sistemas de navegación de los vehículos generar cada vez mejores trayectorias libres de colisiones. Esta habilidad también permite a los vehículos autónomos planificar rutas óptimas, evitar obstáculos, seguir algún objetivo, o muchas otras tareas. Inicialmente, el interés sobre los vehículos autónomos recaía principalmente en las aplicaciones militares, especialmente en los vehículos aéreos, conocidos como UAVs o “Drones”. Pero con el paso del tiempo, las aplicaciones civiles o domésticas están sobre pasando los intereses militares. Estas aplicaciones incluyen tanto a vehículos terrestres como aéreos, aunque el impacto sobre los vehículos autónomos aéreos (UAVs) es mucho mayor. Esto es debido a que la accesibilidad y maniobrabilidad de estos vehículos ofrece más ventajas que los vehículos autónomos terrestres (UGVs) en aplicaciones como localización, seguimiento, adquisición de imágenes, generación de mapas, etc. Esta tesis doctoral presenta un nuevo método centralizado para la generación de trayectorias para múltiples vehículos autónomos. Este método se puede usar tanto para vehículos terrestres como aéreos, e incluso en escenarios mixtos con ambos tipos de vehículos. Dicho método está basado en los métodos de colocación Pseudoespectrales, más conocido en inglés como “Pseudospectral (PS) collocation methods”. Estos métodos son muy utilizados para resolver problemas de control óptimos, y se caracterizar porque resuelven dicho problema numéricamente. En el caso de generación de trayectorias, el problema es formulado como un problema de control óptimo, incluyendo las ecuaciones diferenciales que definen la dinámica de los vehículos, las propias restricciones físicas de los actuadores del vehículo, así como las dimensiones del escenario y restricciones de distancia de seguridad entre los distintos vehículos. Luego, se define una función de costes que debe de ser optimizada, como por ejemplo, la distancia de navegación o el propio consumo del vehículo. Los métodos de colocación Pseudospectrales tratan de resolver el problema de optimización aproximando el vector de estado y de control por una serie de polinomios en una serie de puntos denominados puntos de colocación o “collocation points” en inglés. Las restricciones dinámicas de movimiento y las restricciones del problema también deben de cumplirse en dichos puntos. De esta manera, cuando el problema está discretizado y parametrizado, se produce una transformación al paradigma algebraico. Todo el problema se transforma en un problema de Programación no lineal (PNL), el cual será resuelto por algún programa de optimización como por ejemplo puede ser el “SNOPT solver”. Esta forma concreta de modelado del problema de generación de trayectorias permite obtener trayectorias mucho más realistas que son a su vez, más fácil de seguir por el vehículo en cuestión. Esta tesis presenta también un profundo estudio del comportamiento de los distintos métodos de colocación cuando son usados como generadores de trayectorias. A lo largo de la tesis se ha visto que aspectos como la discretización o la aproximación polinómica afectan a la solución del problema, y se ha analizado cómo afecta a otros aspectos como la integridad del sistema, escalabilidad del método (como influye el incremento de vehículos considerados en la planificación), tiempo de computo necesario para obtener una solución, etc. Un resumen de los objetivos que se han abarcado durante el desarrollo de la tesis se presenta a continuación: • Clasificación exhaustiva de los distintos métodos de colocación. Este punto intenta hacer una distinción entre clásicos métodos de colocación Directos y los nuevos Pseudoespectrales. Presentando una descripción completa de estos últimos. • Análisis de los métodos de colocación en problemas de generación de trayectorias. Los métodos de colocación son métodos de propósito general, de manera que se pretende analizar las ventajas y desventajas de estos métodos en los problemas de generación de trayectorias. • Estudio de rendimiento de los métodos de colocación. Aspectos como la calidad de las soluciones obtenidas, escalabilidad, tiempo de cómputo para obtener una solución, aplicaciones de tiempo real, etc. son estudiados en los distintos métodos. • Búsqueda de configuraciones que mejoren el rendimiento. En este apartado se pretende sintonizar los parámetros de configuración de algunos métodos de colocación para buscar un óptimo rendimiento. • Desarrollo de un nuevo algoritmo denominado método de colocación S-Adaptive. Este es un algoritmo desarrollado específicamente para la generación de trayectorias. Este método resuelve toda las desventajas que se producen en los métodos de colocación clásicos. • Desarrollo de escenarios con vehículos terrestres en presencia de obstáculos. Los métodos de colocación han sido muy utilizados en aplicaciones aeronáuticas. Un claro ejemple de ello es la gran cantidad de artículos que se pueden encontrar en la literatura. Es por esto que el uso de vehículos terrestres y concretamente, su uso en presencia de múltiple obstáculos fijos en dichos escenarios, supone una novedad en sí. • Validación experimental de los algoritmos. Este punto se centra en la validación de los resultados obtenidos en las fases de desarrollo y simulación, con vehículos reales. Una gran cantidad de escenarios son presentados con vehículos autónomos, tanto terrestres como aéreos. Todos estos experimentos están dentro del marco de desarrollo del proyecto europeo de investigación EC-SAFEMOBIL “Estimation and Control for SAFE wireless high MOBILity cooperative industrial systems”

Traffic-Aware Ecological Cruising Control for Connected Electric Vehicle

The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle

Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

Autonomous Trajectory Planning and Guidance Control for Launch Vehicles

This open access book highlights the autonomous and intelligent flight control of future launch vehicles for improving flight autonomy to plan ascent and descent trajectories onboard, and autonomously handle unexpected events or failures during the flight. Since the beginning of the twenty-first century, space launch activities worldwide have grown vigorously. Meanwhile, commercial launches also account for the booming trend. Unfortunately, the risk of space launches still exists and is gradually increasing in line with the rapidly rising launch activities and commercial rockets. In the history of space launches, propulsion and control systems are the two main contributors to launch failures. With the development of information technologies, the increase of the functional density of hardware products, the application of redundant or fault-tolerant solutions, and the improvement of the testability of avionics, the launch losses caused by control systems exhibit a downward trend, and the failures induced by propulsion systems become the focus of attention. Under these failures, the autonomous planning and guidance control may save the missions. This book focuses on the latest progress of relevant projects and academic studies of autonomous guidance, especially on some advanced methods which can be potentially real-time implemented in the future control system of launch vehicles. In Chapter 1, the prospect and technical challenges are summarized by reviewing the development of launch vehicles. Chapters 2 to 4 mainly focus on the flight in the ascent phase, in which the autonomous guidance is mainly reflected in the online planning. Chapters 5 and 6 mainly discuss the powered descent guidance technologies. Finally, since aerodynamic uncertainties exert a significant impact on the performance of the ascent / landing guidance control systems, the estimation of aerodynamic parameters, which are helpful to improve flight autonomy, is discussed in Chapter 7. The book serves as a valuable reference for researchers and engineers working on launch vehicles. It is also a timely source of information for graduate students interested in the subject

Saving Fuel for Heavy-Duty Vehicles Using Connectivity and Automation

The booming of e-commerce is placing an increasing burden on freight transport system by demanding faster and larger amount of delivery. Despite the variety in freight transport means, the dominant freight transport method is still ground transport, or specifically, transport by heavy-duty vehicles. Roughly one-third of the annual ground freight transport expense goes to fuel expenses. If fuel costs could be reduced, the finance of freight transport would be improved and may increase the transport volume without additional charge to average consumers. A further benefit of reducing fuel consumption would be the related environmental impact. The fuel consumption of the heavy-duty vehicles, despite being the minority of road vehicles, has a major influence on the whole transportation sector, which is a major contributor to greenhouse gas emissions. Thus, saving fuel for heavy-duty trucks would also reduce greenhouse gas emission, leading to environmental benefits. For decades, researchers and engineers have been seeking to improve the fuel economy of heavy-duty vehicles by focusing on vehicles themselves, working on advancing the vehicle design in many aspects. More recently, attention has turned to improve fuel efficiency while driving in the dynamic traffic environment. Fuel savings effort may be realized due to advancements in connected and automated vehicle technologies, which provide more information for vehicle design and control. This dissertation presents state-of-the-art techniques that utilize connectivity and automation to improve the fuel economy of heavy-duty vehicles, while allowing them to stay safe in real-world traffic environments. These techniques focus on three different levels of vehicle control, and can result in significant fuel improvements at each level. Starting at the powertrain level, a gear shift schedule design approach is proposed based on hybrid system theory. The resulting design improves fuel economy without comprising driveability. This new approach also unifies the gear shift logic design of human-driven and automated vehicles, and shows a large potential in fuel saving when enhanced with higher level connectivity and automation. With this potential in mind, at the vehicle level, a fuel-efficient predictive cruise control algorithm is presented. This mechanism takes into account road elevation, wind, and aggregated traffic information acquired via connectivity. Moreover, a systematic tool to tune the optimization parameters to prioritize different objectives is developed. While the algorithm and the tool are shown to be beneficial for heavy-duty vehicles when they are in mild traffic, such benefits may not be attainable when the traffic is dense. Thus, at the traffic level, when a heavy-duty vehicle needs to interact with surrounding vehicles in dense traffic, a connected cruise control algorithm is proposed. This algorithm utilizes beyond-line-of-sight information, acquired through vehicle-to-vehicle communication, to gain a better understanding of the surrounding traffic so that the vehicle can response to traffic in a fuel efficient way. These techniques can bring substantial fuel economy improvements when applied individually. In practice, it is important to integrate these three techniques at different levels in a safe manner, so as to acquire the overall benefits. To achieve this, a safety verification method is developed for the connected cruise control, to coordinate the algorithms at the vehicle level and the traffic level, maximizing the fuel benefits while staying safe.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147523/1/hchaozhe_1.pd

Fuel Consumption Reduction Through Velocity Optimization for Light-Duty Autonomous Vehicles with Different Energy Sources

The emergence of self-driving cars provides an additional flexibility to the vehicle controller, by eliminating the driver and allowing for control of the vehicle's velocity. This work employs constrained optimal control techniques with preview of position constraints, to derive optimal velocity trajectories in a longitudinal vehicle following mode. A framework is developed to compare autonomous driving to human driving, i.e. the Federal Test Procedures of the US Environmental Protection Agency. With just velocity smoothing, improvements by offline global optimization of up to 18% in Fuel Economy (FE), are shown for certain drive cycles in a baseline gasoline vehicle. Applying the same problem structure in an online optimal controller with 1.5 s preview showed a 12% improvement in FE. This work is further extended by using a lead velocity prediction algorithm that provides inaccurate future constraints. For a 10 s prediction horizon, a 10% improvement in FE has been shown. A more conventional procedure for achieving velocity optimization would be the minimization of energy demand at the wheels. This method involves a non-linear model thus increasing optimization complexity and also requires additional information about the vehicle such as mass and drag coefficients. It is shown that even though tractive energy minimization has a lower energy demand than velocity smoothing, smoothing works as well if not better when it comes to reducing fuel consumption. These results are shown to be valid in simulation across three different engines ranging from 1.2 L-turbocharged to 4.3 L-naturally aspirated. The implication of these results is that tractive energy minimization requiring more complex control does not work well for conventional gasoline vehicles. It is further shown that using reduced order powertrain models currently found in literature for velocity optimization, can result in worse FE than previous optimizations. Therefore, an easily implementable, vehicle agnostic velocity smoothing algorithm could be preferred for drive cycle optimization. Employing these same velocity optimization techniques for a battery electric vehicle (BEV) can increase battery range by 15%. It is further demonstrated that eco-driving and regenerative braking are not complimentary and eco-driving is always preferred. Finally, power split optimization has been carried out for a fuel cell hybrid, and it has been shown that a rule-based strategy with drive cycle preview could match the global optimal results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149826/1/niketpr_1.pd

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Periodic Control of Automotive Vehicles to Improve Fuel Economy

This research studies the intersection of two technologies to improve fuel economy, i.e., pulse-and-glide (PnG) and cooperative adaptive cruise control (CACC). By exploiting the characteristics of internal combustion engines (ICEs), PnG periodically turns on and off the engine to save fuel. On the other hand, CACC facilitates the vehicle platooning via vehicle-to-vehicle (V2V) communication. CACC is promising to both increase the traffic throughput and reduce the fuel consumption. This research explores the possibilities for more fuel saving potential by introducing PnG into CACC. It also addresses the speed oscillation problem resulting from PnG operations, which is a challenge to vehicle platooning in terms of both string stability and ride comfort. To address these challenges, first the PnG operation of a hybrid electric vehicle (HEV) in the car-following scenario is studied with ride comfort considerations. The proposed control consists of two minimum-time control problems, one for the pulsing phase and another for the gliding phase. These two problems are solved using model-predictive control (MPC). After a series of simplification, convexification, and sparsity optimization, the two minimum-time control problems are reformulated as quadratic programming (QP) problems using the pseudo-spectral (PS) method to be solved on-line efficiently. This proposed control establishes a framework that can effectively leverage PnG for fuel savings, while satisfying the ride comfort and safety constraints. For the problem of platooning heterogeneous PnG vehicles, the concept of PnG synchronization is proposed as a solution. A control approach is developed based on the Kuramoto oscillator model to realize this concept. More specifically, individual vehicles in the platoon maintain their own virtual oscillators. With the synchronization mechanism provided by the Kuramoto model, the virtual oscillators are synchronized via only local communications. By tracking the target trajectories given by the virtual oscillators, PnG synchronization is achieved. A range-keeping approach via V2V communication is also developed. This proposed method of PnG synchronization is able to maintain the fuel saving potentials of individual PnG vehicles while keeping the platoon compact, which is ideal for achieving high throughput. The naturalistic driving data from the Safety Pilot project are utilized to analyze the levels of acceleration that people experience in everyday driving. Also, a PnG experiment is conducted using an automated Lincoln MKZ. The results from this experiment validate the fuel saving ability of the proposed PnG technique, especially at lower speeds, and offer a better knowledge about the influence of PnG operations on ride comfort.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169892/1/syshieh_1.pd

Coordination on Systems of Multiple UAVs

Esta tesis trata acerca de métodos para coordinar las trayectorias de un sistema de Vehículos Aéreos no Tripulados y Autónomos (en adelante UAVs). El primer conjunto de técnicas desarrolladas durante la tesis se agrupan dentro de las técnicas de planificación de trayectorias. En este caso, el objetivo es generar planes de vuelo para un conjunto de vehículos coordinadamente de forma que no se produzcan colisiones entre ellos. Además, este tipo de técnicas puede usarse para modificar el plan de vuelo de un subconjunto de UAVs en tiempo real. Entre los algoritmos desarrollados en la tesis podemos destacar la adaptación de algoritmos evolutivos como los Algoritmos Genéticos y el Particle Swarm (Enjambre de Partículas), la incorporación de nuevas formas de muestreo del espacio para la aplicación del algoritmo Optimal Rapidly Exploring Random Trees (RRT*) en sistemas multi-UAV usando técnicas de muestreo novedosas. También se ha estudiado el comportamiento de parte de estos algoritmos en situaciones variables de incertidumbre del estado del sistema. En particular, se propone el uso del Filtro de Partículas para estimar la posición relativa entre varios UAVs. Además, se estudia la aplicación de métodos reactivos para la resolución de colisiones en tiempo real. Esta tesis propone un nuevo algoritmo para la resolución de colisiones entre múltiples UAVs en presencia de obstáculos fijos llamado G-ORCA. Este algoritmo soluciona varios problemas que han surgido al aplicar el algoritmo ORCA en su variante 3D en sistemas compuestos por vehículos reales. Su seguridad se ha demostrado tanto analíticamente, como empíricamente en pruebas con sistemas reales. De hecho, durante esta tesis numerosos experimentos en sistemas multi-UAV reales compuestos hasta por 4 UAVs han sido ejecutados. En dichos experimentos, se realiza una coordinación autónoma de UAVs en las que se asegura la ejecución de trayectorias libres de colisiones garantizando por tanto la seguridad del sistema. Una característica reseñable de esta tesis es que los algoritmos desarrollados han sido probados e integrados en sistemas más complejos que son usados en aplicaciones reales. En primer lugar, se presenta un sistema para aumentar la duración del vuelo de planeadores aprovechando las corrientes ascendentes de viento generadas por el calor (térmicas). En segundo lugar, un sistema de detección y resolución de colisiones coordinado para sistemas con múltiples UAVs reactivo ha sido diseñado, desarrollado y probado experimentalmente. Este sistema ha sido integrado dentro de un sistema automático de construcción de estructuras mediante múltiples UAVs.The aim of this thesis is to propose methods to coordinately generate trajectories for a system of Autonomous Unmanned Aerial Vehicles (UAVs). The first set of proposed techniques developed in this thesis can be defined as trajectory planning techniques. In this case, the objective is to generate coordinated flight plans for a system of UAVs in such a way that no collision are produced among each pair of UAVs. Besides, these techniques can be applied online in order to modify the original flight plan whenever a potential collision is detected. Amongst the developed algorithms in this thesis we can highlight the adaptation of evolutionary algorithms such as Genetic Algorithms and Particle Swarm, and the application of Optimal Rapidly Exploring Random Trees (RRT*) algorithm into a system of several UAVs with novel sampling techniques. In addition, many of these techniques have been adapted in order to be applicable when only uncertain knowledge of the state of the system is available. In particular, the use of the Particle Filter is proposed in order to estimate the relative position between UAVs. The estimation of the position as well as the uncertainty related to this estimation are then taken into account in the conflict resolution system. All techniques proposed in this thesis have been validated by performing several simulated and real tests. For this purpose, a method for randomly generating a huge test batch is presented in chapter 3. This will allow to test the behavior of the proposed methods in a great variety of situations. During the thesis, several real experimentations with fleets composed by up to four UAVs are presented. In these experiments, the UAVs in the system are automatically coordinated in order to ensure collision-free trajectories and thus guarantee the safety of the system. The other main topic of this thesis is the application of reactive methods for real-time conflict resolution. This thesis proposes a novel algorithm for collision resolution amongst multiple UAVs in the presence of static obstacles, which has been called Generalized-Optimal Reciprocal Collision Avoidance (G-ORCA). This algorithm overcomes several issues that have been detected into the algorithm 3D-ORCA in real applications. A remarkable characteristic of this thesis is that the developed algorithms have been applied as a part of more complex systems. First, a coordinated system for flight endurance extension of gliding aircrafts by profiting the ascending wind is presented. Second, a reactive collision avoidance block has been designed, developed and tested experimentally based in the aforementioned G-ORCA algorithm. This block has been integrated into a system for assembly construction with multiple UAVs