Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things

This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Using digitalisation for data-driven freight curbside management. A perspective from urban transport planning

Given trends in urbanisation, e-commerce, active mobility and modal shifts, streets have sprung up as scenes of conflict where competing demands for curbside space have increased. Because public space is limited, urban transport planners are called to solve public space conflicts by defining how much space is allocated to specific users as a means to achieve sustainable cities. In the allocation of curbside space, freight parking operations are sometimes overlooked compared to other curbside uses such as private vehicles parking. However, limited space for freight deliveries generates negative impacts on urban traffic (e.g. due to double parking), as well as on emissions and companies’ efficiency (e.g. due to the need to cruise for parking). This thesis aims to contribute to current understandings of the need for and uses of data to inform curbside management decision-making for freight parking from the perspective of urban transport planning. To that end, a case study was conducted to collect and analyse data about freight curbside operations using quantitative and qualitative methods, and a cross-sectional research design facilitated the exploration of the impacts of curbside interventions on cities’ sustainability worldwide

Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Planning for Density in a Driverless World

Automobile-centered, low-density development was the defining feature of population growth in the United States for decades. This development pattern displaced wildlife, destroyed habitat, and contributed to a national loss of biodiversity. It also meant, eventually, that commutes and air quality worsened, a sense of local character was lost in many places, and the negative consequences of sprawl impacted an increasing percentage of the population. Those impacts led to something of a shift in the national attitude toward sprawl. More people than ever are fluent in concepts of “smart growth,” “new urbanism,” and “green building,” and with these tools and others, municipalities across the country are working to redevelop a central core, rethink failing transit systems, and promote pockets of density. Changing technology may disrupt this trend. Self-driving vehicles are expected to be widespread within the next several decades. Those vehicles will likely reduce congestion, air pollution, and deaths, and free up huge amounts of productive time in the car. These benefits may also eliminate much of the conventional motivation and rationale behind sprawl reduction. As the time-cost of driving falls, driverless cars have the potential to incentivize human development of land that, by virtue of its distance from settled metropolitan areas, had been previously untouched. From the broader ecological perspective, each human surge into undeveloped land results in habitat destruction and fragmentation, and additional loss of biological diversity. New automobile technology may therefore usher in better air quality, increased safety, and a significant threat to ecosystem health. Our urban and suburban environments have been molded for centuries to the needs of various forms of transportation. The same result appears likely to occur in response to autonomous vehicles, if proactive steps are not taken to address their likely impacts. Currently, little planning is being done to prepare for driverless technology. Actors at multiple levels, however, have tools at their disposal to help ensure that new technology does not come at the expense of the nation’s remaining natural habitats. This Article advocates for a shift in paradigm from policies that are merely anti-car to those that are pro-density, and provides suggestions for both cities and suburban areas for how harness the positive aspects of driverless cars while trying to stem the negative. Planning for density regardless of technology will help to ensure that, for the world of the future, there is actually a world

Shared autonomous vehicle services: A comprehensive review

© 2019 Elsevier Ltd The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed