Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs

Automated highway systems : platoons of vehicles viewed as a multiagent system

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2005-2006La conduite collaborative est un domaine lié aux systèmes de transport intelligents, qui utilise les communications pour guider de façon autonome des véhicules coopératifs sur une autoroute automatisée. Depuis les dernières années, différentes architectures de véhicules automatisés ont été proposées, mais la plupart d’entre elles n’ont pas, ou presque pas, attaqué le problème de communication inter véhicules. À l’intérieur de ce mémoire, nous nous attaquons au problème de la conduite collaborative en utilisant un peloton de voitures conduites par des agents logiciels plus ou moins autonomes, interagissant dans un même environnement multi-agents: une autoroute automatisée. Pour ce faire, nous proposons une architecture hiérarchique d’agents conducteurs de voitures, se basant sur trois couches (couche de guidance, couche de management et couche de contrôle du trafic). Cette architecture peut être utilisée pour développer un peloton centralisé, où un agent conducteur de tête coordonne les autres avec des règles strictes, et un peloton décentralisé, où le peloton est vu comme une équipe d’agents conducteurs ayant le même niveau d’autonomie et essayant de maintenir le peloton stable.Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) that makes use of communications to autonomously guide cooperative vehicles on an Automated Highway System (AHS). For the past decade, different architectures of automated vehicles have been proposed, but most of them did not or barely addressed the inter-vehicle communication problem. In this thesis, we address the collaborative driving problem by using a platoon of cars driven by more or less autonomous software agents interacting in a Multiagent System (MAS) environment: the automated highway. To achieve this, we propose a hierarchical driving agent architecture based on three layers (guidance layer, management layer and traffic control layer). This architecture can be used to develop centralized platoons, where the driving agent of the head vehicle coordinates other driving agents by applying strict rules, and decentralized platoons, where the platoon is considered as a team of driving agents with a similar degree of autonomy, trying to maintain a stable platoon

5G-Enabled Autonomous Platooning on Robotic Vehicle Testbed

Humanity is progressively moving towards a more intuitive and technological future. The area of Intelligent and Cooperative Transport Systems has revealed itself as one of the areas in great evolution, through technologies of autonomous driving and intravehicle communication. With the main goal of providing accident-free environments as well as optimizing the movement of vehicles on roads all over the world, Vehicle to Everything (V2X) communication is very important when it comes to all kinds of vehicular applications. The CMU|PT FLOYD project focuses on this area, with the aim of developing new systems for possible future implementation. In this report, a vehicular application using a 5G-capable module to perform Vehicle to Infrastructure (V2I) communications was evaluated. This vehicular application is based on an emergency braking scenario, whereby detecting an approaching vehicle in a place where an accident occurred, a message is sent over the network that is picked up by the main vehicle, triggering braking. It should be noted that this sending will be made through the module with 5G capacity, thus being an innovative application. Complementary to this scenario is the tracking of a vehicle by another vehicle, thus making a more complex emergency braking application with a cooperative platoon. This platoon will be maintained through sensors present in the following vehicle, such as LiDAR and ZED camera. With this, image processing and a sensor fusion was done in order to keep the follower at a safe distance but with the ability to follow the leader. In order to validate and test this entire solution, robotic testbeds were used as a low-cost solution, allowing a concrete evaluation, with enlightening physical results of the entire application performed.A humanidade, está a caminhar, progressivamente, para um futuro mais intuitivo e tecnológico. A área dos Sistemas Inteligentes e Cooperativos de Transporte tem-se revelado como uma das áreas em grande evolução, através de tecnologias de condução autónoma e comunicação intra-veicular. Com o objetivo principal de proporcionar ambientes sem acidentes, assim como otimizar o movimento de veículos nas estradas de todo o mundo, a comunicação V2X é muito importante no que toca a todo o tipo de aplicações veiculares. O projeto CMU|PT FLOYD centra-se nesta mesma área, com o intuito de desenvolver novos sistemas de possível implementação futura. Neste relatório, é avaliada assim uma aplicação veicular utilizando um módulo com capacidade 5G para realizar comunicações V2I. Essa aplicação veicular baseiase num cenário de travagem de emergência, em que ao detetar uma aproximação de um veículo num local onde ocorreu um acidente, é enviada uma mensagem pela rede que é captada pelo veículo principal, despoletando a travagem. De destacar que este envio será feito através do módulo com capacidade 5G, sendo desta forma uma aplicação inovadora. Complementado a este cenário está a realização do seguimento de um veículo por parte de um outro veículo, tornando assim uma aplicação mais complexa de travagem de emergência com um pelotão cooperativo. Este pelotão será mantido através de sensores presentes no veículo seguidor como o LiDAR e a ZED camera. Com isto, foi utilizado processamento de imagem e foi feita a fusão de sensores de forma a manter o seguidor a uma distância de segurança mas com capacidade de seguir o líder. Com o objetivo de validar e testar toda esta solução, foram utilizadas plataformas robóticas como solução de baixo custo, permitindo assim ter uma avaliação concreta, com resultados físicos esclarecedores de toda a aplicação realizada