Multiple criteria-based real-time decision making by autonomous city vehicles

This paper addresses the topic of real-time decision making for autonomous city vehicles, i.e. the autonomous vehicles’ ability to make appropriate driving decisions in city road traffic situations. After decomposing the problem into two consecutive decision making stages, and giving a short overview about previous work, the paper explains how Multiple Criteria Decision Making (MCDM) can be used in the process of selecting the most appropriate driving maneuver.

Real-time decision making by driverless city vehicles : a discrete event driven approach

This thesis addresses the topic of real-time decision making by driverless (autonomous) city vehicles, i.e. their ability to make appropriate driving decisions in non-simplified urban traffic conditions. After addressing the state of research, and explaining the research question, the thesis presents solutions for the subcomponents which are relevant for decision making with respect to information input (World Model), information output (Driving Maneuvers), and the real-time decision making process. TheWorld Model is a software component developed to fulfill the purpose of collecting information from perception and communication subsystems, maintaining an up-to-date view of the vehicle’s environment, and providing the required input information to the Real-Time Decision Making subsystem in a well-defined, and structured way. The real-time decision making process consists of two consecutive stages. While the first decision making stage uses a Petri net to model the safetycritical selection of feasible driving maneuvers, the second stage uses Multiple Criteria Decision Making (MCDM) methods to select the most appropriate driving maneuver, focusing on fulfilling objectives related to efficiency and comfort. The complex task of autonomous driving is subdivided into subtasks, called driving maneuvers, which represent the output (i.e. decision alternatives) of the real-time decision making process. Driving maneuvers are considered as implementations of closed-loop control algorithms, each capable of maneuvering the autonomous vehicle in a specific traffic situation. Experimental tests in both a 3D simulation and real-world experiments attest that the developed approach is suitable to deal with the complexity of real-world urban traffic situations.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Game-­theoretic Resource Allocation with Real-­time Probabilistic Surveillance Information

Game-theoretic security resource allocation problems have generated significant interest in the area of designing and developing security systems. These approaches traditionally utilize the Stackelberg game model for security resource scheduling in order to improve the protection of critical assets. The basic assumption in Stackelberg games is that a defender will act first, then an attacker will choose their best response after observing the defender’s strategy commitment (e.g., protecting a specific asset). Thus, it requires an attacker’s full or partial observation of a defender’s strategy. This assumption is unrealistic in real-time threat recognition and prevention. In this paper, we propose a new solution concept (i.e., a method to predict how a game will be played) for deriving the defender’s optimal strategy based on the principle of acceptable costs of minimax regret. Moreover, we demonstrate the advantages of this solution concept by analyzing its properties.

Road traffic information platform for energy and emissions savings

Apesar das recentes inovações tecnológicas, o setor dos transportes continua a exercer impactes significativos sobre a economia e o ambiente. Com efeito, o sucesso na redução das emissões neste setor tem sido inferior ao desejável. Isto deve-se a diferentes fatores como a dispersão urbana e a existência de diversos obstáculos à penetração no mercado de tecnologias mais limpas. Consequentemente, a estratégia “Europa 2020” evidencia a necessidade de melhorar a eficiência no uso das atuais infraestruturas rodoviárias. Neste contexto, surge como principal objetivo deste trabalho, a melhoria da compreensão de como uma escolha de rota adequada pode contribuir para a redução de emissões sob diferentes circunstâncias espaciais e temporais. Simultaneamente, pretende-se avaliar diferentes estratégias de gestão de tráfego, nomeadamente o seu potencial ao nível do desempenho e da eficiência energética e ambiental. A integração de métodos empíricos e analíticos para avaliação do impacto de diferentes estratégias de otimização de tráfego nas emissões de CO2 e de poluentes locais constitui uma das principais contribuições deste trabalho. Esta tese divide-se em duas componentes principais. A primeira, predominantemente empírica, baseou-se na utilização de veículos equipados com um dispositivo GPS data logger para recolha de dados de dinâmica de circulação necessários ao cálculo de emissões. Foram percorridos aproximadamente 13200 km em várias rotas com escalas e características distintas: área urbana (Aveiro), área metropolitana (Hampton Roads, VA) e um corredor interurbano (Porto-Aveiro). A segunda parte, predominantemente analítica, baseou-se na aplicação de uma plataforma integrada de simulação de tráfego e emissões. Com base nesta plataforma, foram desenvolvidas funções de desempenho associadas a vários segmentos das redes estudadas, que por sua vez foram aplicadas em modelos de alocação de tráfego. Os resultados de ambas as perspetivas demonstraram que o consumo de combustível e emissões podem ser significativamente minimizados através de escolhas apropriadas de rota e sistemas avançados de gestão de tráfego. Empiricamente demonstrou-se que a seleção de uma rota adequada pode contribuir para uma redução significativa de emissões. Foram identificadas reduções potenciais de emissões de CO2 até 25% e de poluentes locais até 60%. Através da aplicação de modelos de tráfego demonstrou-se que é possível reduzir significativamente os custos ambientais relacionados com o tráfego (até 30%), através da alteração da distribuição dos fluxos ao longo de um corredor com quatro rotas alternativas. Contudo, apesar dos resultados positivos relativamente ao potencial para a redução de emissões com base em seleções de rotas adequadas, foram identificadas algumas situações de compromisso e/ou condicionantes que devem ser consideradas em futuros sistemas de eco navegação. Entre essas condicionantes importa salientar que: i) a minimização de diferentes poluentes pode implicar diferentes estratégias de navegação, ii) a minimização da emissão de poluentes, frequentemente envolve a escolha de rotas urbanas (em áreas densamente povoadas), iii) para níveis mais elevados de penetração de dispositivos de eco-navegação, os impactos ambientais em todo o sistema podem ser maiores do que se os condutores fossem orientados por dispositivos tradicionais focados na minimização do tempo de viagem. Com este trabalho demonstrou-se que as estratégias de gestão de tráfego com o intuito da minimização das emissões de CO2 são compatíveis com a minimização do tempo de viagem. Por outro lado, a minimização de poluentes locais pode levar a um aumento considerável do tempo de viagem. No entanto, dada a tendência de redução nos fatores de emissão dos poluentes locais, é expectável que estes objetivos contraditórios tendam a ser minimizados a médio prazo. Afigura-se um elevado potencial de aplicação da metodologia desenvolvida, seja através da utilização de dispositivos móveis, sistemas de comunicação entre infraestruturas e veículos e outros sistemas avançados de gestão de tráfego.

A traffic differentiation add-on to the IEEE 802.15.4 protocol: implementation and experimental validation over a real-time operating system

The IEEE 802.15.4 is the most widespread used protocol for Wireless Sensor Networks (WSNs) and it is being used as a baseline for several higher layer protocols such as ZigBee, 6LoWPAN or WirelessHART. Its MAC (Medium Access Control) supports both contention-free (CFP, based on the reservation of guaranteed time-slots GTS) and contention based (CAP, ruled by CSMA/CA) access, when operating in beacon-enabled mode. Thus, it enables the differentiation between real-time and best-effort traffic. However, some WSN applications and higher layer protocols may strongly benefit from the possibility of supporting more traffic classes. This happens, for instance, for dense WSNs used in time-sensitive industrial applications. In this context, we propose to differentiate traffic classes within the CAP, enabling lower transmission delays and higher success probability to timecritical messages, such as for event detection, GTS reservation and network management. Building upon a previously proposed methodology (TRADIF), in this paper we outline its implementation and experimental validation over a real-time operating system. Importantly, TRADIF is fully backward compatible with the IEEE 802.15.4 standard, enabling to create different traffic classes just by tuning some MAC parameters.

Preliminary results from a real time control system using optical information

Successful results from training an adaptive controller to use optical information to balance an inverted pendulum are presented in comparison to the training requirements using traditional controller inputs. Results from research into the psychology of the sense of balance in humans are presented as the motivation for the investigation of this new type of controller. The simulated model of the inverted pendulum and the virtual reality environments used to provide the optical input are described The successful introduction of optical information is found to require the preservation of at least two of the traditional input types and entail increased training time for the adaptive controller and reduced performance (measured as the time the pendulum remains upright).

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Mobile application as a tool for urban traffic data collection and generation to advanced traveler information systems using Wi-Fi networks available in urban centers

This paper presents an Advanced Traveler Information System (ATIS) developed on Android platform, which is open source and free. The developed application has as its main objective the free use of a Vehicle-to- Infrastructure (V2I) communication through the wireless network access points available in urban centers. In addition to providing the necessary information for an Intelligent Transportation System (ITS) to a central server, the application also receives the traffic data close to the vehicle. Once obtained this traffic information, the application displays them to the driver in a clear and efficient way, allowing the user to make decisions about his route in real time. The application was tested in a real environment and the results are presented in the article. In conclusion we present the benefits of this application. © 2012 IEEE.

Information Filtering for Ultrasound-based Real-time Registration

This paper presents methods based on Information Filters for solving matching problems with emphasis on real-time, or effectively real-time applications. Both applications discussed in this work deal with ultrasound-based rigid registration in computer-assisted orthopedic surgery. In the first application, the usual workflow of rigid registration is reformulated such that registration algorithms would iterate while the surgeon is acquiring ultrasound images of the anatomy to be operated. Using this effectively real-time approach to registration, the surgeon would then receive feedback in order to better gauge the quality of the final registration outcome. The second application considered in this paper circumvents the need to attach physical markers to bones for anatomical referencing. Experiments using anatomical objects immersed in water are performed in order to evaluate and compare the different methods presented herein, using both 2D as well as real-time 3D ultrasound.