AutoDRIVE: A Comprehensive, Flexible and Integrated Cyber-Physical Ecosystem for Enhancing Autonomous Driving Research and Education

Prototyping and validating hardware-software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things

Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment

Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. The supplementary video can be viewed at https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc

A modular traffic sampling architecture for flexible network measurements

Dissertação de Mestrado (Programa Doutoral em Informática)The massive traffic volumes and the heterogeneity of services in today’s networks urge for flexible, yet simple measurement solutions to assist network management tasks, without impairing network performance. To turn treatable tasks requiring traffic analysis, sampling the traffic has become mandatory, triggering substantial research in the area. In fact, multiple sampling techniques have been proposed to assist network engineering tasks, each one targeting specific measurement goals and traffic scenarios. Despite that, there is still a lack of an encompassing solution able to support the flexible deployment of these techniques in production networks. In this context, this research work proposes a modular traffic sampling architecture able to foster the flexible design and deployment of efficient measurement strategies. The architecture is composed of three layers i.e., management plane, control plane and data plane covering key components to achieve versatile and lightweight measurements in diverse traffic scenarios and measurement activities. The flexibility and modularity in deploying different sampling strategies relies upon a novel taxonomy of sampling techniques, in which, current and emerging techniques are identified regarding their inner characteristics - granularity, selection trigger and selection scheme. Following the proposed taxonomy, a sampling framework prototype has been developed and used as an experimental implementation of the proposed architecture, providing a fair environment to assess and compare sampling techniques under distinct measurement scenarios. Supported by the sampling framework, distinct techniques have been evaluated regarding their performance in balancing the computational burden and the accuracy in supporting traffic workload estimation and flow analysis. The results have demonstrated the relevance and applicability of the proposed architecture, revealing that a modular and configurable approach to sampling is a step forward for improving sampling scope and efficiency.Os grandes volumes de tráfego e a heterogeneidade de serviços nas redes atuais requerem soluções de medição que sejam flexíveis e simples de modo a sustentar as tarefas de gestão de redes sem afetar o desempenho das mesmas. Para tornar tratável as tarefas que exigem análise de tráfego, tornou-se obrigatório recorrer a amostragem do tráfego, motivando uma investigação substancial na área. Como consequência, várias técnicas de amostragem foram propostas para auxiliar as tarefas de engenharia de redes, cada uma orientada a satisfazer objetivos de medição e cenários de tráfego específicos. Apesar disso, ainda não existe uma solução abrangente capaz de suportar a implantação flexível destas técnicas em redes de produção. Neste contexto, este trabalho propõe uma arquitetura modular de amostragem de tráfego capaz de fomentar a concepção flexível e a implementação de estratégias efi- cientes de medição de tráfego. A arquitetura é composta por três camadas, nomeadamente, camada de gestão, camada de controle e camada de dados, cobrindo os principais componentes para alcançar versatilidade e baixo custo computacional em variados cenários de tráfego e atividades de medição. A flexibilidade e modularidade na implementação de diferentes técnicas de amostragem baseia-se numa nova taxonomia, na qual técnicas atuais e emergentes são identificadas de acordo com suas características internas - granularidade, trigger de seleção e esquema de seleção. Seguindo a taxonomia proposta, um protótipo estruturando e agregando as diferentes técnicas de amostragem foi desenvolvido e utilizado na implementação experimental da arquitetura, permitindo avaliar e comparar as técnicas de amostragem em diversos cenários de medição. Suportado pelo protótipo desenvolvido, distintas técnicas foram avaliadas quanto ao seu desempenho em equilibrar a carga computacional e a acurácia na estimação do volume de tráfego e na análise de fluxos. Os resultados demonstraram a relevância e aplicabilidade da arquitetura de amostragem proposta, revelando que uma abordagem modular e configurável constitui um avanço no sentido de melhorar a eficiência na amostragem de tráfego

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

DRIVE: A Digital Network Oracle for Cooperative Intelligent Transportation Systems

In a world where Artificial Intelligence revolutionizes inference, prediction and decision-making tasks, Digital Twins emerge as game-changing tools. A case in point is the development and optimization of Cooperative Intelligent Transportation Systems (C-ITSs): a confluence of cyber-physical digital infrastructure and (semi)automated mobility. Herein we introduce Digital Twin for self-dRiving Intelligent VEhicles (DRIVE). The developed framework tackles shortcomings of traditional vehicular and network simulators. It provides a flexible, modular, and scalable implementation to ensure large-scale, city-wide experimentation with a moderate computational cost. The defining feature of our Digital Twin is a unique architecture allowing for submission of sequential queries, to which the Digital Twin provides instantaneous responses with the "state of the world", and hence is an Oracle. With such bidirectional interaction with external intelligent agents and realistic mobility traces, DRIVE provides the environment for development, training and optimization of Machine Learning based C-ITS solutions.Comment: Accepted for publication at IEEE ISCC 202

Scalable and Energy Efficient Software Architecture for Human Behavioral Measurements

Understanding human behavior is central to many professions including engineering, health and the social sciences, and has typically been measured through surveys, direct observation and interviews. However, these methods are known to have drawbacks, including bias, problems with recall accuracy, and low temporal fidelity. Modern mobile phones have a variety of sensors that can be used to find activity patterns and infer the underlying human behaviors, placing a heavy load on the phone's battery. Social science researchers hoping to leverage this new technology must carefully balance the fidelity of the data with the cost in phone performance. Crucially, many of the data collected are of limited utility because they are redundant or unnecessary for a particular study question. Previous researchers have attempted to address this problem by modifying the measurement schedule based on sensed context, but a complete solution remains elusive. In the approach described here, measurement is made contingent on sensed context and measurement objectives through extensions to a configuration language, allowing significant improvement to flexibility and reliability. Empirical studies indicate a significant improvement in energy efficiency with acceptable losses in data fidelity