Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is provided. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations

Pareto Optimal Strategies for Event Triggered Estimation

Although resource-limited networked autonomous systems must be able to efficiently and effectively accomplish tasks, better conservation of resources often results in worse task performance. We specifically address the problem of finding strategies for managing measurement communication costs between agents. A well understood technique for trading off communication costs with estimation accuracy is event triggering (ET), where measurements are only communicated when useful, e.g., when Kalman filter innovations exceed some threshold. In the absence of measurements, agents can use implicit information to achieve results almost as well as when explicit data is always communicated. However, there are no methods for setting this threshold with formal guarantees on task performance. We fill this gap by developing a novel belief space discretization technique to abstract a continuous space dynamics model for ET estimation to a discrete Markov decision process, which scalably accommodates threshold-sensitive ET estimator error covariances. We then apply an existing probabilistic trade-off analysis tool to find the set of all optimal trade-offs between resource consumption and task performance. From this set, an ET threshold selection strategy is extracted. Simulated results show our approach identifies non-trivial trade-offs between performance and energy savings, with only modest computational effort.Comment: 8 pages, accepted to IEEE Conference on Decision and Control 202

On Quality in Radiotherapy Treatment Plan Optimisation

Radiotherapy is one of the essential treatments used in the fight against cancer. The goal of radiotherapy is to deliver a high dose of ionising radiation to the tumour volume and at the same time minimise the effect on healthy tissue by reducing the radiation to critical organs. This contradiction is challenging and has been driving the research and development of the treatments.Over the last two decades, there has been tremendous technical development inradiotherapy. The rapid increase in computational power introduced treatment plan optimisation and intensity-modulated radiotherapy (IMRT). IMRT made it possible to shape the radiation dose distribution closely around the target volume avoiding critical organs to a greater extent. Rotational implementation of IMRT, e.g. Volumetric Modulated Arc Therapy (VMAT) further improved this “dose shaping” ability. With these techniques increasing the ability to produce better treatment plans, there was a need for evaluation tools to compare the treatment plan quality. A plan can be judged by how well it fulfils the prescription and dose-volume constraints, ideally based on treatment outcome. In this work, this is denoted Required Plan Quality, the minimum quality to accept a plan for clinical treatment. If a plan does not fulfil all the dose-volume constraints, there should be a clear priority of which constraints are crucial to achieve. On the other hand, if the constraints are easily fulfilled, there might be a plan of better quality only limited by the treatment systems ability to find and deliver it. This is denoted Attainable Plan Quality in this work– the quality possible to achieve with a given treatment system for a specific patient group.In work described in this thesis, the so-called Pareto front method was used to search for the attainable plan quality to compare different treatment planning systems and optimisation strategies. More specifically, a fall-back planning system for backup planning and an optimiser to find the best possible beam angles. The Pareto method utilises a set of plans to explore the trade-off between target and nearby risk organs.The Pareto plan generation is time-consuming if done manually. The Pareto method was then used in a software that automated the plan generation allowing for a more accurate representation of the trade-off. The software was used to investigate the attainable plan quality for prostate cancer treatments. In the last two publications in this thesis, machine learning approaches were developed to predict a treatment plancloser to the attainable plan quality compared to a manually generated plan.In the thesis, tools have been developed to help move the treatment plan qualityfrom Required Plan Quality towards the Attainable Plan Quality, i.e. the best quality we can achieve with our current system

Contributions to the Optimisation of aircraft noise abatement procedures

Tot i que en les últimes dècades la reducció del soroll emès pels avions ha estat substancial, el seu impacte a la població ubicada a prop dels aeroports és un problema que encara persisteix. Contenir el soroll generat per les operacions d'aeronaus, tot assumint al mateix temps la creixent demanda de vols, és un dels principals desafiaments a que s'enfronten les autoritats aeroportuàries, els proveïdors de serveis per a la navegació aèria i els operadors de les aeronaus. A part de millorar l'aerodinàmica o les emissions sonores de les aeronaus, l'impacte acústic de les seves operacions es pot reduir també gràcies a la definició de nous procediments de vol més òptims. Aquests procediments s'anomenen generalment Procediments d'Atenuació de Soroll (PAS) i poden incloure rutes preferencials de vol (a fi d'evitar les zones poblades) i també perfils de vol verticals optimitzats. Els procediments actuals per a la reducció de soroll estan molt lluny de ser els òptims. En general, la seva optimització no és possible a causa de les limitacions d'avui en dia en els mètodes de navegació, els equips d'aviònica i la complexitat present en alguns espais aeris. D'altra banda, molts PAS s'han dissenyat de forma manual per un grup d'experts i amb l'ajuda de diverses iteracions. Tot i això, en els propers anys s'esperen nous sistemes d'aviònica i conceptes de gestió del trànsit aeri que permetin millorar el disseny d'aquests procediments, fent que siguin més flexibles. En els pocs casos on s'optimitzen PAS, se sol utilitzar una mètrica acústica en l'elaboració de les diferents funcions objectiu i per tant, no es tenen en compte les molèsties sonores reals. La molèstia és un concepte subjectiu, complexe i que depèn del context en que s'usa i la seva integració en l'optimització de trajectòries segueix essent un aspecte a estudiar.La present tesi doctoral es basa en el fet que en el futur serà possible definir trajectòries més flexibles i precises. D'aquesta manera es permetrà la definició de procediments de vol òptims des d'un punt de vista de molèsties acústiques. Així doncs, es considera una situació en que aquest tipus de procediments poden ser dissenyats de forma automàtica o semi-automàtica per un sistema expert basat en tècniques d'optimització i de raonament aproximat. Això serviria com una eina de presa de decisions per planificadors de l'espai aeri i dissenyadors de procediments. En aquest treball es desenvolupa una eina completa pel càlcul de PAS òptims. Això inclou un conjunt de models no lineals que tinguin en compte la dinàmica de les aeronaus, les limitacions de la trajectòria i les funcions objectiu. La molèstia del soroll es modela utilitzant tècniques de lògica difusa en funció del nivell màxim de so percebut, l'hora del dia i el tipus de zona a sobrevolar. Llavors, s'identifica i es formula formalment el problema com a un problema de control òptim multi-criteri. Per resoldre'l es proposa un mètode de transcripció directa per tal de transformar-lo en un problema de programació no lineal. A continuació s'avaluen una sèrie de tècniques d'optimització multi-objectiu i entre elles es destaca el mètode d'escalarització, el més utilitzat en la literatura. No obstant això, s'exploren diverses tècniques alternatives que permeten superar certs inconvenients que l'escalarització presenta. En aquest context, es presenten i proven tècniques d'optimització lexicogràfica, jeràrquica, igualitària (o min-max) i per objectius. D'aquest anàlisi es desprenen certes conclusions que permeten aprofitar les millors característiques de cada tècnica i formar finalment una tècnica composta d'optimització multi-objectiu. Aquesta última estratègia s'aplica amb èxit a un escenari real i complex, on s'optimitzen les sortides cap a l'Est de la pista 02 de l'aeroport de Girona. En aquest exemple, dos tipus diferents d'aeronaus volant a diferents períodes del dia són simulats obtenint, conseqüentment, diferents trajectòries òptimes.Aunque en las últimas décadas la reducción del ruido emitido por los aviones ha sido sustancial, su impacto en la población ubicada cerca de los aeropuertos es un problema persistente. Contener este ruido, asumiendo al mismo tiempo la creciente demanda de vuelos, es uno de los principales desafíos a que se enfrentan las autoridades aeroportuarias, los proveedores de servicios para la navegación y los operadores. Aparte de mejorar la aerodinámica o las emisiones sonoras de las aeronaves, su impacto acústico se puede reducir también gracias a la definición de nuevos procedimientos de vuelo optimizados. Éstos, se denominan generalmente Procedimientos de Atenuación de Ruido (PAR) y pueden incluir rutas preferenciales de vuelo (a fin de evitar las zonas pobladas) y también perfiles de vuelo optimizados.Los procedimientos actuales para la reducción de ruido están muy lejos de ser los óptimos. En general, su optimización no es posible debido a las limitaciones de hoy en día en los métodos de navegación, los equipos de aviónica y la complejidad presente en algunos espacios aéreos. Por otra parte, muchos PAR se han diseñado de forma manual por un grupo de expertos y con la ayuda de varias iteraciones. Sin embargo, en los próximos años se esperan nuevos sistemas de aviónica y conceptos de gestión del tráfico aéreo que permitan mejorar el diseño de estos procedimientos, haciendo que sean más flexibles. En los pocos casos donde se optimizan PAR, se suele utilizar una métrica acústica en la elaboración de las diferentes funciones objetivo y por lo tanto, no se tienen en cuenta las molestias sonoras reales. La molestia es un concepto subjetivo, complejo y que depende del contexto en que se usa y su integración en la optimización de trayectorias sigue siendo un aspecto a estudiar. La presente tesis doctoral se basa en el hecho de que en el futuro será posible definir trayectorias más flexibles y precisas. De esta manera se permitirá la definición de procedimientos de vuelo óptimos desde un punto de vista de molestias acústicas. Se considera una situación en que este tipo de procedimientos pueden ser diseñados de forma automática o semi-automática por un sistema experto basado en técnicas de optimización y de razonamiento aproximado. Esto serviría como una herramienta de toma de decisiones para planificadores del espacio aéreo y diseñadores de procedimientos.En este trabajo se desarrolla una herramienta completa para el cálculo de PAR óptimos. Esto incluye un conjunto de modelos no lineales que tengan en cuenta la dinámica de las aeronaves, las limitaciones de la trayectoria y las funciones objetivo. La molestia del ruido se modela utilizando técnicas de lógica difusa en función del nivel máximo de sonido percibido, la hora del día y el tipo de zona a sobrevolar. Entonces, se identifica y se formula formalmente el problema como un problema de control óptimo multi-criterio. Para resolverlo se propone un método de transcripción directa para transformarlo en un problema de programación no lineal. A continuación se evalúan una serie de técnicas de optimización multi-objetivo y entre ellas se destaca el método de escalarización, el más utilizado en la literatura. Sin embargo, se exploran diversas técnicas alternativas que permiten superar ciertos inconvenientes que la escalarización presenta. En este contexto, se presentan y prueban técnicas de optimización lexicográfica, jerárquica, igualitaria (o min-max) y por objetivos. De este análisis se desprenden ciertas conclusiones que permiten aprovechar las mejores características de cada técnica y formar finalmente una técnica compuesta de optimización multi-objetivo. Esta última estrategia se aplica con éxito en un escenario real y complejo, donde se optimizan las salidas hacia el Este de la pista 02 del aeropuerto de Girona. En este ejemplo, dos tipos diferentes de aeronaves volando a diferentes periodos del día son simulados obteniendo, consecuentemente, diferentes trayectorias óptimas.Despite the substantial reduction of the emitted aircraft noise in the last decades, the noise impact on communities located near airports is a problem that still lingers. Containing the sound generated by aircraft operations, while meeting the increasing demand for aircraft transportation, is one of the major challenges that airport authorities, air traffic service providers and aircraft operators may deal with. Aircraft noise can be reduced by improving the aerodynamics of the aircraft, the engine noise emissions but also in designing new optimised flight procedures. These procedures, are generally called Noise Abatement Procedures (NAP) and may include preferential routings (in order to avoid populated areas) and also schedule optimised vertical flight path profiles. Present noise abatement procedures are far from being optimal in regards to minimising noise nuisances. In general, their optimisation is not possible due to the limitations of navigation methods, current avionic equipments and the complexity present at some terminal airspaces. Moreover, NAP are often designed manually by a group of experts and several iterations are needed. However, in the forthcoming years, new avionic systems and new Air Traffic Management concepts are expected to significantly improve the design of flight procedures. This will make them more flexible, and therefore will allow them to be more environmental friendly. Furthermore, in the few cases where NAP are optimised, an acoustical metric is usually used when building up the different optimisation functions. Therefore, the actual noise annoyance is not taken into account in the optimisation process. The annoyance is a subjective, complex and context-dependent concept. Even if sophisticated noise annoyance models are already available today, their integration into an trajectory optimisation framework is still something to be further explored. This dissertation is mainly focused on the fact that those precise and more flexible trajectories will enable the definition of optimal flight procedures regarding the noise annoyance impact, especially in the arrival and departure phases of flights. In addition, one can conceive a situation where these kinds of procedures can be designed automatically or semi-automatically by an expert system, based on optimisation techniques and approximate reasoning. This would serve as a decision making tool for airspace planners and procedure designers.A complete framework for computing optimal NAP is developed in this work. This includes a set of nonlinear models which take into account aircraft dynamics, trajectory constraints and objective functions. The noise annoyance is modelled by using fuzzy logic techniques in function of the perceived maximum sound level, the hour of the day and the type of over-flown zone. The problem tackled, formally identified and formulated as a multi-criteria optimal control problem, uses a direct transcription method to transform it into a Non Linear Programming problem. Then, an assessment of different multi-objective optimisation techniques is presented. Among these techniques, scalarisation methods are identified as the most widely used methodologies in the present day literature. Yet, in this dissertation several alternative techniques are explored in order to overcome some known drawbacks of this technique. In this context, lexicographic, hierarchical, egalitarian (or min-max) and goal optimisation strategies are presented and tested. From this analysis some conclusions arise allowing us to take advantage of the best features of each optimisation technique aimed at building a final compound multi-objective optimisation strategy. Finally, this strategy is applied successfully to a complex and real scenario, where the East departures of runway 02 at the airport of Girona (Catalonia, Spain) are optimised. Two aircraft types are simulated at different periods of the day obtaining different optimal trajectories.Postprint (published version

Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information

Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route

Mechanism and Behaviour Co-optimisation of High Performance Mobile Robots

Mobile robots do not display the level of physical performance one would expect, given the specifications of their hardware. This research is based on the idea that their poor performance is at least partly due to their design, and proposes an optimisation approach for the design of high-performance mobile robots. The aim is to facilitate the design process, and produce versatile and robust robots that can exploit the maximum potential of today's technology. This can be achieved by a systematic optimisation study that is based on careful modelling of the robot's dynamics and its limitations, and takes into consideration the performance requirements that the robot is designed to meet. The approach is divided into two parts: (1) an optimisation framework, and (2) an optimisation methodology. In the framework, designs that can perform a large set of tasks are sought, by simultaneously optimising the design and the behaviours to perform them. The optimisation methodology consists of several stages, where various techniques are used for determining the design's most important parameters, and for maximising the chances of finding the best possible design based on the designer's evaluation criteria. The effectiveness of the optimisation approach is proved via a specific case-study of a high-performance balancing and hopping monopedal robot. The outcome is a robot design and a set of optimal behaviours that can meet several performance requirements of conflicting nature, by pushing the hardware to its limits in a safe way. The findings of this research demonstrate the importance of using realistic models, and taking into consideration the tasks that the robot is meant to perform in the design process

Motion Planning for Autonomous Vehicles in Partially Observable Environments

Unsicherheiten, welche aus Sensorrauschen oder nicht beobachtbaren Manöverintentionen anderer Verkehrsteilnehmer resultieren, akkumulieren sich in der Datenverarbeitungskette eines autonomen Fahrzeugs und führen zu einer unvollständigen oder fehlinterpretierten Umfeldrepräsentation. Dadurch weisen Bewegungsplaner in vielen Fällen ein konservatives Verhalten auf. Diese Dissertation entwickelt zwei Bewegungsplaner, welche die Defizite der vorgelagerten Verarbeitungsmodule durch Ausnutzung der Reaktionsfähigkeit des Fahrzeugs kompensieren. Diese Arbeit präsentiert zuerst eine ausgiebige Analyse über die Ursachen und Klassifikation der Unsicherheiten und zeigt die Eigenschaften eines idealen Bewegungsplaners auf. Anschließend befasst sie sich mit der mathematischen Modellierung der Fahrziele sowie den Randbedingungen, welche die Sicherheit gewährleisten. Das resultierende Planungsproblem wird mit zwei unterschiedlichen Methoden in Echtzeit gelöst: Zuerst mit nichtlinearer Optimierung und danach, indem es als teilweise beobachtbarer Markov-Entscheidungsprozess (POMDP) formuliert und die Lösung mit Stichproben angenähert wird. Der auf nichtlinearer Optimierung basierende Planer betrachtet mehrere Manöveroptionen mit individuellen Auftrittswahrscheinlichkeiten und berechnet daraus ein Bewegungsprofil. Er garantiert Sicherheit, indem er die Realisierbarkeit einer zufallsbeschränkten Rückfalloption gewährleistet. Der Beitrag zum POMDP-Framework konzentriert sich auf die Verbesserung der Stichprobeneffizienz in der Monte-Carlo-Planung. Erstens werden Informationsbelohnungen definiert, welche die Stichproben zu Aktionen führen, die eine höhere Belohnung ergeben. Dabei wird die Auswahl der Stichproben für das reward-shaped Problem durch die Verwendung einer allgemeinen Heuristik verbessert. Zweitens wird die Kontinuität in der Reward-Struktur für die Aktionsauswahl ausgenutzt und dadurch signifikante Leistungsverbesserungen erzielt. Evaluierungen zeigen, dass mit diesen Planern große Erfolge in Fahrversuchen und Simulationsstudien mit komplexen Interaktionsmodellen erreicht werden

OPTIMIZATION OF TERMINAL LAYOUTS: AN ANALYTICAL AND SIMULATIVE APPROACH BASED ON GENETIC ALGORITHMS

2012/2013Every day millions of pedestrian move with different needs and objectives through spaces each of them with its functional specifications. An accurate design or revisiting of transport terminals, as for example railway stations, underway stations, airports, as well as complex buildings, open spaces and a deep analysis of public events with relevant pedestrian flows, would improve its usability at users benefit. To reach this goal is necessary a careful integration among architecture, engineering needs and transport disciplines, that, starting from the study of users behavior and pedestrian dynamics, provides the fundamental elements to be considered during design stage to ensure a major level of service. In literature nothing much is known about the optimal dimension of pedestrian transportation terminals. The aim of this study is to develop a methodology to size the functional terminal layouts, by the integration of analytical and simulative models submitted to generic algorithms, taking into account the dynamics and flows generated inside the terminals. In order to obviate the lack of requisite data for models calibration, validation and verification, as well as testing the process developed, an algorithm for data acquisition has been elaborated. It has a dedicated graphic interface, which allows to reveal the pedestrian dynamics and consequently to generate database; with these data is possible to obtain statistical and behavioral indicators about pedestrians detected. The use of analytical models, both to define the sizing of facilities inside the terminals and to model the user behavior during their paths, allows to define an objective function able to represent the performances of the terminal functional layout. Defined the dimensional ranges of each functional element inside the layout according a specific Level of Service, performed a design of experiments methodology and applied genetic algorithms to minimize the objective function, it is possible to obtain a set of optimal solutions for the terminal configuration sizing, in coherence with flows and dynamics generated inside the terminals itself. A further simulative approach, based on the application of the social force algorithm, allows, through quantitative and qualitative parameters, to identify the best solution(s) inside the domain previously identified with genetic algorithm application. Starting from the motivation that inspired this work, analyzed the existing literature and the main methods for data acquisition, it will be introduced the algorithm for the automatic acquisition of data and pedestrian database generation. The application of this tool will be illustrated in order to manifest the potentiality of the instrument same. Subsequently introduced the tool developed for the definition of the characteristic elements sizing and the model chosen for the correct estimation of pedestrian travel times, it will be explored the structure of the objective function aimed to identify the right trade-off between infrastructure and pedestrian costs. Finally, the application of genetic algorithms, resulting in the identification of Pareto front, generates the domain of optimal solutions to sift through the simulation approach. The developed methodology reveals a flexible and simple instruments, but, at the same time, accurate in the resolution of the problems for which has been structured. The potential of the developed methodology is highlighted in the course of the work thanks to a case of study.Ogni giorno milioni di pedoni si muovono con esigenze ed obbiettivi diversi in contesti differenti, ognuno dei quali con le sue caratteristiche tecniche funzionali. Un’attenta progettazione o rivisitazione dei terminali di trasporto, quali stazioni ferroviarie, metropolitane, aeroporti, così come degli edifici complessi, degli spazi aperti ed una corretta disamina degli eventi pubblici con flussi pedonali rilevanti, consentirebbe di migliorarne la fruibilità a beneficio dell’utenza. Per raggiungere tale obiettivo risulta necessaria un’attenta integrazione tra esigenze architettoniche, ingegneristiche e le discipline trasportistiche, le quali, partendo dallo studio comportamentale degli utenti e dalle dinamiche pedonali, forniscano gli elementi fondamentali da tenersi in considerazione nella fase di progettazione per garantire un maggiore livello di servizio. Riscontrata in letteratura una carenza di approcci finalizzata alla determinazione del miglior layout funzionale dei terminali, attraverso l’integrazione di modelli analitici e simulativi sottoposti ad algoritmi genetici, è stata sviluppata una metodologia che, coerentemente con le dinamiche e i flussi che all’interno dei terminali stessi si generano, mirasse al dimensionamento ottimo dei terminali di trasporto pedonale. Per ovviare alla mancanza di dati necessari per i processi di calibrazione, validazione e verifica dei modelli così come per testare il metodo sviluppato è stato innanzitutto elaborato un algoritmo per l’acquisizione di dati, con interfaccia grafica dedicata, che consente di rilevare le dinamiche pedonali, generare database e conseguentemente ricavare dati statistici e comportamentali dei pedoni. L’utilizzo di modelli analitici, sia per l’identificazione dei range dimensionali degli elementi caratteristici presenti all’interno dei terminali che per la modellizzazione del comportamento degli utenti, permette di definire una funzione obbiettivo che rappresenti le performances dei layout funzionali dei terminali. Attraverso design of experiments calibrati sui range dimensionali dei singoli elementi funzionali presenti all’interno dei terminali e la successiva applicazione degli algoritmi genetici finalizzati alla minimizzazione della funzione obiettivo, è possibile definire un insieme di soluzioni ottime per il dimensionamento dei terminali, in coerenza con i flussi e le dinamiche che in esso si generano. Un’ulteriore approccio simulativo, basato sull’applicazione dell’algoritmo delle forze sociali, consente, attraverso la valutazione di parametri quantitativi e qualitativi, di identificare la/e miglior soluzione/i all’interno del dominio di soluzioni precedentemente identificate con l’applicazione degli algoritmi genetici. A partire dall’esplicitazione delle motivazioni che hanno alimentato questo lavoro, analizzata la letteratura esistente e le principali metodologie per l’acquisizione dati, verrà introdotto l’algoritmo per l’acquisizione automatica dei dati pedonali e la generazione di database contenenti i profili degli utenti rilevati. A seguire troverà spazio l’applicazione di questo strumento per manifestarne le potenzialità. Successivamente, introdotto il tool sviluppato per la definizione dei range dimensionali degli elementi caratteristici e il modello scelto per la corretta stima dei tempi di percorrenza pedonali, verrà esplorata la strutturazione della funzione obiettivo finalizzata alla ricerca del giusto trade off tra costi infrastrutturali e pedonali. Infine, l’applicazione degli algoritmi genetici, risultanti nell’identificazione del fronte paretiano, genererà il dominio di soluzioni ottime da vagliare attraverso l’approccio simulativo. La metodologia sviluppata si è rivelata uno strumento flessibile ed agevole, ma, allo stesso tempo, puntuale nel risolvere i problemi per cui è stata ideata. Le potenzialità della metodologia sviluppata vengono messe in risalto nel corso dell’elaborato grazie ad un caso di studio condotto.XXVI Ciclo198

Assessing the effectiveness of managed lane strategies for the rapid deployment of cooperative adaptive cruise control technology

Connected and Automated Vehicle (C/AV) technologies are fast expanding in the transportation and automotive markets. One of the highly researched examples of C/AV technologies is the Cooperative Adaptive Cruise Control (CACC) system, which exploits various vehicular sensors and vehicle-to-vehicle communication to automate vehicular longitudinal control. The operational strategies and network-level impacts of CACC have not been thoroughly discussed, especially in near-term deployment scenarios where Market Penetration Rate (MPR) is relatively low. Therefore, this study aims to assess CACC\u27s impacts with a combination of managed lane strategies to provide insights for CACC deployment. The proposed simulation framework incorporates 1) the Enhanced Intelligent Driver Model; 2) Nakagami-based radio propagation model; and 3) a multi-objective optimization (MOOP)-based CACC control algorithm. The operational impacts of CACC are assessed under four managed lane strategies (i.e., mixed traffic (UML), HOV (High Occupancy Vehicle)-CACC lane (MML), CACC dedicated lane (DL), and CACC dedicated lane with access control (DLA)). Simulation results show that the introduction of CACC, even with 10% MPR, is able to improve the network throughput by 7% in the absence of any managed lane strategies. The segment travel times for both CACC and non-CACC vehicles are reduced. The break-even point for implementing dedicated CACC lane is 30% MPR, below which the priority usage of the current HOV lane for CACC traffic is found to be more appropriate. It is also observed that DLA strategy is able to consistently increase the percentage of platooned CACC vehicles as MPR grows. The percentage of CACC vehicles within a platoon reaches 52% and 46% for DL and DLA, respectively. When it comes to the impact of vehicle-to-vehicle (V2V), it is found that DLA strategy provides more consistent transmission density in terms of median and variance when MPR reaches 20% or above. Moreover, the performance of the MOOP-based cooperative driving is examined. With average 75% likelihood of obtaining a feasible solution, the MOOP outperforms its counterpart which aims to minimize the headway objective solely. In UML, MML, and DL strategy, the proposed control algorithm achieves a balance spread among four objectives for each CACC vehicle. In the DLA strategy, however, the probability of obtaining feasible solution falls to 60% due to increasing size of platoon owing to DLA that constraints the feasible region by introduction more dimensions in the search space. In summary, UML or MML is the preferred managed lane strategy for improving traffic performance when MPR is less than 30%. When MRP reaches 30% or above, DL and DLA could improve the CACC performance by facilitating platoon formation. If available, priority access to an existing HOV lane can be adopted to encourage adaptation of CACC when CACC technology becomes publically available

Machine learning model selection with multi-objective Bayesian optimization and reinforcement learning

A machine learning system, including when used in reinforcement learning, is usually fed with only limited data, while aimed at training a model with good predictive performance that can generalize to an underlying data distribution. Within certain hypothesis classes, model selection chooses a model based on selection criteria calculated from available data, which usually serve as estimators of generalization performance of the model. One major challenge for model selection that has drawn increasing attention is the discrepancy between the data distribution where training data is sampled from and the data distribution at deployment. The model can over-fit in the training distribution, and fail to extrapolate in unseen deployment distributions, which can greatly harm the reliability of a machine learning system. Such a distribution shift challenge can become even more pronounced in high-dimensional data types like gene expression data, functional data and image data, especially in a decentralized learning scenario. Another challenge for model selection is efficient search in the hypothesis space. Since training a machine learning model usually takes a fair amount of resources, searching for an appropriate model with favorable configurations is by inheritance an expensive process, thus calling for efficient optimization algorithms. To tackle the challenge of distribution shift, novel resampling methods for the evaluation of robustness of neural network was proposed, as well as a domain generalization method using multi-objective bayesian optimization in decentralized learning scenario and variational inference in a domain unsupervised manner. To tackle the expensive model search problem, combining bayesian optimization and reinforcement learning in an interleaved manner was proposed for efficient search in a hierarchical conditional configuration space. Additionally, the effectiveness of using multi-objective bayesian optimization for model search in a decentralized learning scenarios was proposed and verified. A model selection perspective to reinforcement learning was proposed with associated contributions in tackling the problem of exploration in high dimensional state action spaces and sparse reward. Connections between statistical inference and control was summarized. Additionally, contributions in open source software development in related machine learning sub-topics like feature selection and functional data analysis with advanced tuning method and abundant benchmarking were also made