Modeling present and future freeway management strategies : variable speed limits, lane-changing and platooning of connected autonomous vehicles

Freeway traffic management is necessary to improve capacity and reduce congestion, especially in metropolitan freeways where the rush period lasts several hours per day. Traffic congestion implies delays and an increase in air pollutant emissions, both with harmful effects to society. Active management strategies imply regulating traffic demand and improving freeway capacity. While both aspects are necessary, the present thesis only addresses the supply side. Part of the research in traffic flow theory is grounded on empirical data. Today, in order to extend our knowledge on traffic dynamics, detailed and high-quality data is needed. To that end, the thesis presents a pioneering data collection campaign, which was developed in a freeway accessing Barcelona. In a Variable Speed Limits (VSL) environment, different speed limits where posted, in order to observe their real and detailed effects on traffic. All the installed surveillance instruments were set to capture data in the highest possible level of detail, including video recordings, from where to count lane-changing maneuvers. With this objective, a semi-automatic method to reliably count lane changes form video recordings was developed and is presented in the thesis. Data analysis proved that the speed limit fulfillment was only relevant in sections with enforcement devices. In these sections, it is confirmed that, the lower the speed limit, the higher the occupancy to achieve a given flow. In contrast, the usually assumed mainline metering effect of low speed limits was not relevant. This might be different in case of stretch enforcement. These findings mean that, on the one hand, VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Results also show that low speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rates. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane can be avoided. Further analysis of lane-changing activity allowed unveiling that high lane-changing rates prevent achieving the highest flows. This inverse relationship is modeled in the thesis using a stochastic model based on Bayesian inference. This model could be used as a control tool, in order to determine which level of lane-changing activity can be allowed to achieve a desired capacity with some level of reliability. Previous results identify drivers' fulfillment of traffic regulations as a weak point in order to maximize the benefits of current management strategies, like VSL or lane-changing control. This is likely to change in the near future with the irruption of Autonomous Vehicles (AV) in freeways. V2X communications will allow directly actuating on individual vehicles with high accuracy. This will open the door to new management strategies based on simultaneous communication to groups of AVs and extremely short reaction times, like platooning, which stands out as a strategy with a huge potential to improve freeway traffic. Strings of AVs traveling at extremely short gaps (i.e. platoons) allow achieving higher capacities and lower energy consumption rates. In this context, the thesis presents a parsimonious macroscopic model for AVs platooning in mixed traffic (i.e. platoons of AVs travelling together with human driven vehicles). The model allows determining the average platoon length and reproducing the overall traffic dynamics leading to higher capacities. Results prove that with a 50% penetration rate of AVs in the lane, capacity could reach 3400 veh/h/lane under a cooperative platooning strategy.Per tal de millorar la capacitat i reduir la congestió a les autopistes cal gestionar el trànsit de manera activa. Les estratègies de gestió activa del trànsit són d’especial importància en autopistes metropolitanes. La congestió provoca retards i un increment del consum de combustible que va lligat a unes majors emissions de gasos contaminants, tots amb efectes perniciosos per la societat. La gestió activa del transit requereix regular la demanda i millorar la capacitat de la via. Encara que tots dos aspectes son necessaris, la present tesis només analitza la gestió de l’oferta. Part de la recerca en l’anàlisi i la teoria del trànsit es basa en dades empíriques. Per satisfer el requeriment de dades detallades i d’alta qualitat, aquesta tesis presenta una campanya pionera de recol·lecció de dades. Les dades es van recollir a l’autopista B-23 d’accés a Barcelona. Tots els instruments de mesura es van configurar per tal de registrar les dades amb el major nivell de detall possible, incloent les càmeres de videovigilància, d’on es varen extreure els comptatges de canvi de carril. Amb aquest objectiu, es va desenvolupar una metodologia semiautomàtica per comptar canvis de carril a partir de gravacions de trànsit, que es presenta en el cos de la tesi. L’anàlisi de les dades obtingudes ha demostrat que el compliment dels límits de velocitat només resulta rellevant en aquelles seccions que compten amb un radar. És en aquestes seccions on s’ha confirmat que com menor és el límit de velocitat, major es l’ocupació per a un flux donat. Per contra, la hipòtesi habitual de que uns límits de velocitat baixos produeixen una restricció del flux no es va observar de forma rellevant. Aquest comportament podria esser diferent en el cas d’implantar un radar de tram. Els resultats obtinguts també mostren com les diferències de velocitats entre carrils s’incrementen per a límits de velocitat baixos i en condicions de demanda moderada. Això, alhora, incrementa el nombre de canvis de carril. Per contra, els límits de velocitat baixos contribueixen a una distribució de flux més uniforme entre carrils, de forma que es pot evitar la infrautilització de carrils. L’anàlisi més detallat de l’activitat de canvi de carril demostra que una taxa elevada de canvis de carril impedeix assolir fluxos grans de circulació. En la tesi, aquesta relació inversa entre la taxa de canvis de carril i el flux màxim de trànsit a l’autopista s’ha modelat de forma estocàstica utilitzant un model basat en la inferència Bayesiana. Aquest model es pot utilitzar com una eina de control, per tal de determinar quina taxa de canvi de carril es pot permetre si es vol assolir una capacitat determinada amb una determinada probabilitat de compliment. En vista dels resultats previs, la falta de compliment de les normes de trànsit per part dels conductors s’identifica com un punt dèbil a l’hora de maximitzar els beneficis de les actuals estratègies de gestió del transit. Això probablement canviarà en el futur pròxim amb la irrupció dels Vehicles Autònoms (VA) a les autopistes. Els sistemes de comunicació V2X permetran actuar individualment sobre cada vehicle amb una gran precisió. Això obrirà la porta a noves estratègies de gestió, basades en la comunicació simultània entre diferents grups de VA i en temps de reacció extremadament curts, com per exemple és el “platooning”, que destaca pel seu gran potencial per millorar el trànsit en autopista. Els “platons” son cadenes de VA viatjant amb uns espaiaments extremadament curts que permeten assolir capacitats mes elevades i un menor consum energètic. En aquest context, la tesi presenta un model macroscòpic parsimoniós per a “platons” de VA en condicions de transit mixt, és a dir, compartint la infraestructura amb vehicles tradicionals. El model permet determinar la longitud mitjana del “platons” i reproduir el trànsit global dinàmiques que condueixen a majors capacitats. Els resultats demostren que amb un 50% la velocitat de penetració dels AV al carril, la capacitat podria arribar als 3.400 vehicles / h / carril sota una estratègia cooperativa de “platooning

Modeling present and future freeway management strategies : variable speed limits, lane-changing and platooning of connected autonomous vehicles

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit d’Enginyeria Civil i AmbientalFreeway traffic management is necessary to improve capacity and reduce congestion, especially in metropolitan freeways where the rush period lasts several hours per day. Traffic congestion implies delays and an increase in air pollutant emissions, both with harmful effects to society. Active management strategies imply regulating traffic demand and improving freeway capacity. While both aspects are necessary, the present thesis only addresses the supply side. Part of the research in traffic flow theory is grounded on empirical data. Today, in order to extend our knowledge on traffic dynamics, detailed and high-quality data is needed. To that end, the thesis presents a pioneering data collection campaign, which was developed in a freeway accessing Barcelona. In a Variable Speed Limits (VSL) environment, different speed limits where posted, in order to observe their real and detailed effects on traffic. All the installed surveillance instruments were set to capture data in the highest possible level of detail, including video recordings, from where to count lane-changing maneuvers. With this objective, a semi-automatic method to reliably count lane changes form video recordings was developed and is presented in the thesis. Data analysis proved that the speed limit fulfillment was only relevant in sections with enforcement devices. In these sections, it is confirmed that, the lower the speed limit, the higher the occupancy to achieve a given flow. In contrast, the usually assumed mainline metering effect of low speed limits was not relevant. This might be different in case of stretch enforcement. These findings mean that, on the one hand, VSL strategies aiming to restrict the mainline flow on a freeway by using low speed limits will need to be applied carefully, avoiding conditions as the ones presented here. On the other hand, VSL strategies trying to get the most from the increased vehicle storage capacity of freeways under low speed limits might be rather promising. Results also show that low speed limits increase the speed differences across lanes for moderate demands. This, in turn, also increases the lane changing rates. In contrast, lower speed limits widen the range of flows under uniform lane flow distributions, so that, even for moderate to low demands, the under-utilization of any lane can be avoided. Further analysis of lane-changing activity allowed unveiling that high lane-changing rates prevent achieving the highest flows. This inverse relationship is modeled in the thesis using a stochastic model based on Bayesian inference. This model could be used as a control tool, in order to determine which level of lane-changing activity can be allowed to achieve a desired capacity with some level of reliability. Previous results identify drivers' fulfillment of traffic regulations as a weak point in order to maximize the benefits of current management strategies, like VSL or lane-changing control. This is likely to change in the near future with the irruption of Autonomous Vehicles (AV) in freeways. V2X communications will allow directly actuating on individual vehicles with high accuracy. This will open the door to new management strategies based on simultaneous communication to groups of AVs and extremely short reaction times, like platooning, which stands out as a strategy with a huge potential to improve freeway traffic. Strings of AVs traveling at extremely short gaps (i.e. platoons) allow achieving higher capacities and lower energy consumption rates. In this context, the thesis presents a parsimonious macroscopic model for AVs platooning in mixed traffic (i.e. platoons of AVs travelling together with human driven vehicles). The model allows determining the average platoon length and reproducing the overall traffic dynamics leading to higher capacities. Results prove that with a 50% penetration rate of AVs in the lane, capacity could reach 3400 veh/h/lane under a cooperative platooning strategy.Per tal de millorar la capacitat i reduir la congestió a les autopistes cal gestionar el trànsit de manera activa. Les estratègies de gestió activa del trànsit són d’especial importància en autopistes metropolitanes. La congestió provoca retards i un increment del consum de combustible que va lligat a unes majors emissions de gasos contaminants, tots amb efectes perniciosos per la societat. La gestió activa del transit requereix regular la demanda i millorar la capacitat de la via. Encara que tots dos aspectes son necessaris, la present tesis només analitza la gestió de l’oferta. Part de la recerca en l’anàlisi i la teoria del trànsit es basa en dades empíriques. Per satisfer el requeriment de dades detallades i d’alta qualitat, aquesta tesis presenta una campanya pionera de recol·lecció de dades. Les dades es van recollir a l’autopista B-23 d’accés a Barcelona. Tots els instruments de mesura es van configurar per tal de registrar les dades amb el major nivell de detall possible, incloent les càmeres de videovigilància, d’on es varen extreure els comptatges de canvi de carril. Amb aquest objectiu, es va desenvolupar una metodologia semiautomàtica per comptar canvis de carril a partir de gravacions de trànsit, que es presenta en el cos de la tesi. L’anàlisi de les dades obtingudes ha demostrat que el compliment dels límits de velocitat només resulta rellevant en aquelles seccions que compten amb un radar. És en aquestes seccions on s’ha confirmat que com menor és el límit de velocitat, major es l’ocupació per a un flux donat. Per contra, la hipòtesi habitual de que uns límits de velocitat baixos produeixen una restricció del flux no es va observar de forma rellevant. Aquest comportament podria esser diferent en el cas d’implantar un radar de tram. Els resultats obtinguts també mostren com les diferències de velocitats entre carrils s’incrementen per a límits de velocitat baixos i en condicions de demanda moderada. Això, alhora, incrementa el nombre de canvis de carril. Per contra, els límits de velocitat baixos contribueixen a una distribució de flux més uniforme entre carrils, de forma que es pot evitar la infrautilització de carrils. L’anàlisi més detallat de l’activitat de canvi de carril demostra que una taxa elevada de canvis de carril impedeix assolir fluxos grans de circulació. En la tesi, aquesta relació inversa entre la taxa de canvis de carril i el flux màxim de trànsit a l’autopista s’ha modelat de forma estocàstica utilitzant un model basat en la inferència Bayesiana. Aquest model es pot utilitzar com una eina de control, per tal de determinar quina taxa de canvi de carril es pot permetre si es vol assolir una capacitat determinada amb una determinada probabilitat de compliment. En vista dels resultats previs, la falta de compliment de les normes de trànsit per part dels conductors s’identifica com un punt dèbil a l’hora de maximitzar els beneficis de les actuals estratègies de gestió del transit. Això probablement canviarà en el futur pròxim amb la irrupció dels Vehicles Autònoms (VA) a les autopistes. Els sistemes de comunicació V2X permetran actuar individualment sobre cada vehicle amb una gran precisió. Això obrirà la porta a noves estratègies de gestió, basades en la comunicació simultània entre diferents grups de VA i en temps de reacció extremadament curts, com per exemple és el “platooning”, que destaca pel seu gran potencial per millorar el trànsit en autopista. Els “platons” son cadenes de VA viatjant amb uns espaiaments extremadament curts que permeten assolir capacitats mes elevades i un menor consum energètic. En aquest context, la tesi presenta un model macroscòpic parsimoniós per a “platons” de VA en condicions de transit mixt, és a dir, compartint la infraestructura amb vehicles tradicionals. El model permet determinar la longitud mitjana del “platons” i reproduir el trànsit global dinàmiques que condueixen a majors capacitats. Els resultats demostren que amb un 50% la velocitat de penetració dels AV al carril, la capacitat podria arribar als 3.400 vehicles / h / carril sota una estratègia cooperativa de “platooning”Award-winningPostprint (published version

Advances towards behaviour-based indoor robotic exploration

215 p.The main contributions of this research work remain in object recognition by computer vision, by one side, and in robot localisation and mapping by the other. The first contribution area of the research address object recognition in mobile robots. In this area, door handle recognition is of great importance, as it help the robot to identify doors in places where the camera is not able to view the whole door. In this research, a new two step algorithm is presented based on feature extraction that aimed at improving the extracted features to reduce the superfluous keypoints to be compared at the same time that it increased its efficiency by improving accuracy and reducing the computational time. Opposite to segmentation based paradigms, the feature extraction based two-step method can easily be generalized to other types of handles or even more, to other type of objects such as road signals. Experiments have shown very good accuracy when tested in real environments with different kind of door handles. With respect to the second contribution, a new technique to construct a topological map during the exploration phase a robot would perform on an unseen office-like environment is presented. Firstly a preliminary approach proposed to merge the Markovian localisation in a distributed system, which requires low storage and computational resources and is adequate to be applied in dynamic environments. In the same area, a second contribution to terrain inspection level behaviour based navigation concerned to the development of an automatic mapping method for acquiring the procedural topological map. The new approach is based on a typicality test called INCA to perform the so called loop-closing action. The method was integrated in a behaviour-based control architecture and tested in both, simulated and real robot/environment system. The developed system proved to be useful also for localisation purpose

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Modelado de transmisión eficiente de datos para eventos multivariantes basados en umbral

This doctoral thesis delves into the optimization of communications in sensor networks for a specific purpose: to evaluate threshold-based events that depend on multiple distributed variables. This motivation is behind the detailed research presented here in the form of a compendium of papers. The developed work is structured in 3 scientific contributions in articles. Out of those 3 contributions, the most theoretical work has been described in 2 of them, leaving the third article for the presentation of a methodological support tool with great scientific impact and relevance in this doctoral thesis. Due to the two theoretical and large–scale contributions in the proposed field, a solution is proposed which is stated as an hypotheses. The first contribution is the mathematical foundations for modelling data reduction in the sensor network and measuring its influence on the quality of the event evaluation. For this purpose, a set of functions and parameters is defined. This logic modifies the cardinality of the mathematical domains in which information is defined in order to save traffic. Specific metrics that consider the time delays in the state changes of the evaluated condition are also defined. The second contribution is an adaptive algorithm that, taking into account the logical context of the system information, parameterizes the proposed model at runtime. As a result, this technique maximizes traffic reduction and minimizes error in the evaluation of the event simultaneously, obtaining promising results. As a methodological contribution, a procedure for generating pseudo-realistic random signals is also described, a useful tool for easily obtaining large datasets suitable for experimentation, which has been applied in the described contributions.Esta tesis doctoral profundiza en la optimización de las comunicaciones en redes de sensores con un propósito específico: evaluar eventos basados en umbral que dependen de múltiples variables distribuidas. Con esta motivación se desarrolla la investigación detallada aquí en forma compendio de artículos. El trabajo desarrollado se estructura en 3 aportaciones científicas en artículos. De esas 3 aportaciones, el trabajo en su vertiente más teórica se desarrolla en 2 de ellas, quedando el tercer artículo para la presentación de una herramienta de soporte metodológico con gran impacto científico y de relevancia en esta tesis doctoral. Gracias a las dos aportaciones teóricas y de gran calado en el ámbito propuesto se propone una solución que se plantea en forma de hipótesis. La primera aportación son los fundamentos matemáticos para modelar la reducción de datos en la red de sensores y medir su incidencia en la calidad de la evaluación del evento. Para ello define una serie de funciones y parámetros que alteran la cardinalidad de los dominios matemáticos en los que se define la información, así como métricas específicas que tienen en cuenta los desfases temporales en los cambios de estado de la condición evaluada. La segunda aportación es un algoritmo adaptativo que, considerando el contexto lógico de la información del sistema, parametriza el modelo propuesto en tiempo de ejecución. Como resultado, esta técnica maximiza la reducción de tráfico y minimiza el error en la evaluación del evento simultáneamente, obteniendo resultados prometedores. Como tercera aportación se describe también un procedimiento para generar señales aleatorias pseudo–realistas, una herramienta útil para disponer fácilmente de grandes conjuntos de datos adecuados para experimentación, que ha sido utilizada en las aportaciones descritas

CROSS-STACK PREDICTIVE CONTROL FRAMEWORK FOR MULTICORE REAL-TIME APPLICATIONS

Many of the next generation applications in entertainment, human computer interaction, infrastructure, security and medical systems are computationally intensive, always-on, and have soft real time (SRT) requirements. While failure to meet deadlines is not catastrophic in SRT systems, missing deadlines can result in an unacceptable degradation in the quality of service (QoS). To ensure acceptable QoS under dynamically changing operating conditions such as changes in the workload, energy availability, and thermal constraints, systems are typically designed for worst case conditions. Unfortunately, such over-designing of systems increases costs and overall power consumption. In this dissertation we formulate the real-time task execution as a Multiple-Input, Single- Output (MISO) optimal control problem involving tracking a desired system utilization set point with control inputs derived from across the computing stack. We assume that an arbitrary number of SRT tasks may join and leave the system at arbitrary times. The tasks are scheduled on multiple cores by a dynamic priority multiprocessor scheduling algorithm. We use a model predictive controller (MPC) to realize optimal control. MPCs are easy to tune, can handle multiple control variables, and constraints on both the dependent and independent variables. We experimentally demonstrate the operation of our controller on a video encoder application and a computer vision application executing on a dual socket quadcore Xeon processor with a total of 8 processing cores. We establish that the use of DVFS and application quality as control variables enables operation at a lower power op- erating point while meeting real-time constraints as compared to non cross-stack control approaches. We also evaluate the role of scheduling algorithms in the control of homo- geneous and heterogeneous workloads. Additionally, we propose a novel adaptive control technique for time-varying workloads

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Marine invertebrates and sound

Peer ReviewedPostprint (published version

Investigation of a 'Field Factory' to harvest and grade tree stock in a forestry nursery

Primary industries are facing an ever increasing labour problem. Major concerns with labour include lack of staff training, high costs, poor efficiency, non-optimal quality control and health and safety issues. While automation is commonplace in factory environments, such technologies have not yet migrated to an outdoor, agricultural environment. Forestry nurseries are no exception, where the most problematic and labour intensive task is lifting and grading tree stock. Mechanical lifting of tree stock is already performed commercially; however, these machines are incapable of performing the additional steps required by this research, particularly root trimming, coupled with a machine vision system that can replicate the human decision making process for selecting ’good’ and ’bad’ tree stock. In particular, there are strict criteria for root structure which must be assessed. Currently, human graders are proving to be poor assessors of this, to such an extent that tree stock is graded up to three times before being shipped to the customer. Additionally, there is the need to remove expensive pack houses. This research investigates a field factory capable of processing forestry tree stock in the field, from lifting through to grading and boxing. The machine vision component of the field factory was tested in controlled conditions, on a sample of 200 trees. There was good agreement between machine vision measurements and manually measured tree features. There is much ambiguity in the grading process, with three experts only reaching a consensus 75% of the time when grading a sample of trees. The machine vision grading system performed very well, showing less bias than human graders. The machine agreed with the specification 96% of the time, significantly higher than the experts’ agreements of between 86 and 90%. While classification systems such as fuzzy logic and artificial neural networks seem to be a good match for this research, they did not outperform the ’crisp’ grading system. A field factory for harvesting and grading forestry tree stock proved to be feasible; however, further development, particularly on mechanical systems, is required to produce a machine reliable enough to be implemented commercially