Different Levels Of Automation Research Articles

Iterative Function Allocation of Single Pilot Operations Based on Biclustering Method

The successful transition from a two-pilot to a single-pilot mode cannot solely rely on simply augmenting advanced automation in the cockpit. The degree of support provided by functions at different levels of automation varies, and introducing new functions simultaneously brings new demands for the single pilot, leading to imbalanced workload. To address these shortcomings and enhance the rationality of function allocation under workload constraints, this paper introduces an iterative function allocation method. Initially, the functions are categorized and classified into different levels of automation to obtain the initial function allocation scheme. Subsequently, a novel bicluster mining algorithm called K-Miner is proposed to identify elements requiring iterative design. As a case study, the multitask surface scenario in SPO is considered. By applying the proposed iterative design method, the task performance of the single pilot is improved, the workload of the single pilot is effectively reduced, and situational awareness is improved to a certain extent.

A Conceptual Model for Automation of Small Ports

Abstract For large ports and terminals, the loading and discharge operations have to a great extent been automated already. However, for the vast number of small ports, for instance all Norwegian ports, this large-scale automation is not possible due to the small size of the ports compared to the cost of introducing full automation. Still, these small ports are important logistics hubs in the transport system. They facilitate the shift from congested roads to the sea by allowing the goods to arrive closer to the final destination by using smaller, possibly autonomous, vessels. However, this puts pressure on the ports to facilitate reduced laytime in the port, to increase the port efficiency and to improve interoperability towards hinterland systems and terminals. For small ports, this means that manual and automated work processes need to exist side by side, and that it will be important to get an overview of which processes to automate and which will remain manual, in addition to the handling of the hand-over between humans and automation. This paper describes a conceptual model that can be used by small ports as a starting point on their way to introduce automation of their services supporting the cargo flow through the port. The model includes a description of port functions, tasks and responsibilities and use the concept of operational envelope to define the overall capabilities of the system, including both automation and human. This will be the starting point for how to structure the description of an operation centre that monitor and control the maritime terminal operations. The starting point for this work is a use case analysis performed in some small ports in Norway, and their need to optimize the utilization of their assets. The work is done as part of the AutoPort project which is a research project funded by the Norwegian Research Council (project number 337211). The goal of this work is to describe a methodology on how to identify the interface between automation and human operators when it comes to small ports and the movement of cargo through its terminals, and by this give a description on how different levels of automation can be implemented. The methodology consists of several steps, including definition of the context (big picture of the use case, actors and their roles, and communication), the scenario (port activities and processes), and the use cases including the control tasks performed by the automated systems.

Self-Driving Laboratories for Chemistry and Materials Science.

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Heterogeneous traffic flow of expressway with Level 2 autonomous vehicles considering moving bottlenecks

With the development of autonomous driving technology, autonomous vehicles (AV) will gradually replace human-driven vehicles (HDV), but it will still be a mixed traffic flow of autonomous vehicles with different levels of automation for a long time. In addition to cars, trucks regarded as moving bottleneck are also an important part of traffic flow of expressway. In this paper, the elimination rule of moving bottleneck is introduced into the traffic flow model, and the improved hybrid traffic flow cellular automata model is proposed. Next, through numerical simulation, the existence and influence of moving bottleneck caused by heavy duty trucks are hypothesized and verified. The simulation results show that with the proportion of trucks increases, the influence range of moving bottleneck expands, resulting in the decrease of capacity and traffic congestion. Then, the impact of Level 2 autonomous vehicles (hereinafter referred to as L2 AVs) on the traffic flow of moving bottlenecks is explored. The simulation results show that with the proportion of L2 AVs increases, traffic capacity increases, and traffic congestion ratio at medium and high density decreases. Under medium density, the average velocity fluctuates greatly, while under high density, the average velocity decreases due to limitation of density. In summary, L2 AVs can alleviate influence of moving bottlenecks, increase traffic capacity, improve traffic congestion, and lift road safety. The cellular automata model of traffic flow including moving bottleneck proposed in this paper is based on the actual road situation, considering the fact that most of autonomous vehicles on the expressway currently are L2 AVs, and moving bottleneck is mainly caused by heavy trucks. This research contributes to an in-depth analysis of L2 AVs on heterogeneous traffic flows under moving bottleneck environment.

Индикаторы цифрового развития малых форм хозяйствования АПК Волгоградской области

The successful adaptation of the regional agro-industrial complex to a new technological order is impossible without activating the maximum number of resources available to national agriculture: management, production, economic, financial, technological, demographic, and climatic, as well as any other factors that stimulate the transition of the agricultural sector of the Russian economy to a qualitatively new level of scientific and technological development. The fourth technological order in agriculture will become the basis for a qualitatively different level of automation and digitalization of agro-industrial production, within which the role of the business entity will be largely limited only to making key managerial, production, and economic decisions. Technological processes will be fully automated through the widespread use of robotics, digital twins, predictive and cognitive technologies, EFR and CRM systems, the Internet of Things, etc. The need to optimize these solutions and integrate them into a single production and digital network will stimulate the creation of so-called digital ecosystems in the agricultural economy. These ecosystems are “a set of technological platforms, applied Internet services, analytical, and information systems created on the basis of a computer network infrastructure with the characteristics of self-regulation, adaptability, and sustainability for the purpose of joint production or promotion of a given benefit, information exchange, and evolutionary development of the system” aimed at satisfying people’s needs for high-quality and safe food products based on the use of resource-saving and environmentally friendly technologies. The article examines the most significant indicators of the digital development of small agricultural businesses in the context of their ability to function within the framework of regional digital business ecosystems, as well as the prospects for the integration of small businesses into global ecosystems of the digital economy. A set of statistical data was studied that gives an idea of the use of digital technologies in the agricultural sector of the regional economy. A set of statistical data on the use of digital technologies in the agricultural sector of the regional economy has been studied, some indicators of the level of digitalization of small agribusiness entities have been identified and analyzed, making it possible to determine the trajectory of the digital development of micro-farm enterprises and to develop the general contours of a forecast scenario for the integration of small agricultural forms into the digital business ecosystem of the agricultural economy.

Fundamental diagram of mixed traffic flow of CAVs with different connectivity and automation levels

This paper proposes the fundamental diagram model for a mixed traffic flow environment that considers different car-following modes under various levels of automation and connectivity. First, the SAE classification of vehicle automation levels, from L0 to L5, has been further divided into connectivity and automation levels. Second, the car-following modes that affect car-following characteristics are analyzed in mixed traffic flow with different levels of connectivity and automation. Different car-following models are used to describe the car-following characteristics of different types of modes. Third, the fundamental diagram model of mixed traffic flow is further derived based on the different levels of automation and connectivity. Finally, a numerical experiment is designed to evaluate the impact of different penetration rates (PRs) of the car-following modes on the traffic capacity of the mixed traffic flow. The results show that (1) with a higher PR of the automation level of the vehicle, the greater the traffic capacity. At the same time, as the automation levels increase, the change in the penetration rate of high automation levels has a more and more significant impact on traffic capacity. (2) Traffic capacity increases with increased PR of higher automation levels, representing an increase of approximately 11.1 % (3) Compared with connectivity, the PR of automation levels significantly impacts traffic capacity, which is approximately 52.6 %.

The effect of human-machine interface modality, specificity, and timing on driver performance and behavior while using vehicle automation

The effectiveness of the human–machine interface (HMI) in a driving automation system during takeover situations is based, in part, on its design. Past research has indicated that modality, specificity, and timing of the HMI have an impact on driver behavior. The objective of this study was to examine the effectiveness of two HMIs, which vary by modality, specificity, and timing, on drivers’ takeover time, performance, and eye glance behavior. Drivers’ behavior was examined in a driving simulator study with different levels of automation, varying traffic conditions, and while completing a non-driving related task. Results indicated that HMI type had a statistically significant effect on velocity and off-road eye glances such that those who were exposed to an HMI that gave multimodal warnings with greater specificity exhibited better performance. There were no effects of HMI on acceleration, lane position, or other eye glance metrics (e.g., on road glance duration). Future work should disentangle HMI design further to determine exactly which aspects of design yield between safety critical behavior.

The fine line between automation and augmentation in website usability evaluation

Artificial Intelligence (AI) systems are becoming widespread in all aspects of society, bringing benefits to the whole economy. There is a growing understanding of the potential benefits and risks of this type of technology. While the benefits are more efficient decision processes and industrial productivity, the risks may include a potential progressive disengagement of human beings in crucial aspects of decision-making. In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-Centred AI (HCAI) is a novel framework that posits that high levels of human control do not contradict high levels of computer automation. In this paper, we investigate the two antipodes, automation vs augmentation, in the context of website usability evaluation. Specifically, we have analyzed whether the level of automation provided by a tool for semi-automatic usability evaluation can support evaluators in identifying usability problems. Three different visualizations, each one corresponding to a different level of automation, ranging from a full-automation approach to an augmentation approach, were compared in an experimental study. We found that a fully automated approach could help evaluators detect a significant number of medium and high-severity usability problems, which are the most critical in a software system; however, it also emerged that it was possible to detect more low-severity usability problems using one of the augmented approaches proposed in this paper.

Pairing in-vehicle intelligent agents with different levels of automation: implications from driver attitudes, cognition, and behaviors in automated vehicles

ABSTRACT In-vehicle intelligent agents (IVIAs) have been developed to improve user experience in autonomous vehicles. Yet, the impact of the automation system on driver behavior and perception toward IVIAs is unclear. In this study, we conducted three experiments with 73 participants in a driving simulator to examine how automation system parameters (the level of automation system and IVIA features) influence driver attitudes, cognition, and behaviors when driving or riding in a simulated vehicle. We focused on subjective evaluations of driver-agent interaction and driver trust toward IVIAs to assess driver attitudes, driver situation awareness, and visual distraction to capture their cognition, and their driving performance to understand their behaviors. Our results show that the level of automation system affects drivers’ attitudes toward agent capabilities (e.g. perceived intelligence). Embodiment benefits are more pronounced with Level 5 systems, while speech style, in general, is more influential in determining affective aspects of user attitudes (e.g. Warmth, Likability). As the level of automation increases, drivers engage in more visual distractions. In addition, conversational speech style in general encouraged safer driving behaviors indicated by more stable lateral control under lower levels of automation. Our findings uncover the path of how system parameters affect driver behaviors through system evaluation and trust in agents. These findings have important implications for the development of cohesive user experiences in future transportation systems.

European Common Data Management Platform Definition for Railway AI Function Development

Digitalisation and automation of operations in the railway industry include the use of Automatic Train Operation systems that provide automated functions to reach different levels of automation, known as the Grade of Automation (GoA) levels. These levels go up to GoA4 in which the train is automatically controlled without any staff on board. Artificial intelligence has emerged as technology that can substitute humans in certain driving tasks, in GoA3 (driverless) and GoA4 (unattended) modes. AI capabilities include perception, decision-making, precise positioning, or optimization of communications. The success of AI models depends on the quality and diversity of the data used for training, along with the set-up of a data life-cycle framework that covers creation, training, testing, deployment and monitorisation. The management of training datasets implies both expensive and time-consuming data gathering, labelling, curation and formatting efforts, potentially hindering the development of reliable AI systems. This paper presents a Common Data Management Platform developed by a consortium of European railway stakeholders, devised to efficiently manage data for AI training, and which is demonstrated in two different Proofs of Concept.

Rig Automation Empowers Well Construction in Ecuador

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216249, “Digital Innovation of Well-Construction Process in Ecuador Through Rig Automation,” by Karen Peña, Kevin Etcheverry, and Hugo Quevedo, SPE, SLB, et al. The paper has not been peer reviewed. _ Artificial intelligence (AI)-based digital drilling technology was implemented in two mature fields in Ecuador that represent 33% of the country’s oil production. The drilling campaign’s main strategy included the deployment of a novel automation solution on two rigs, resulting in the optimization of the well-construction process. In the complete paper, the authors present the results of implementation of a rig-automation solution applied to 20 wells in 2022. Scope of the Digital Solution The authors detail the validation, optimization, and implementation of a drilling software to automate work flows and the installation and commissioning of associated hardware components. The following key indicators and objectives were established: - Validation of drilling-software work flows - Achievement of a 75% average in automation control - Improvement of rate of penetration (ROP) by 5% - Reduction of pre- and post-connection times by at least 30% - Real-time tuning of parameters - Training of all personnel involved in the use of the drilling-software package - Installation and commission of hardware components Drilling-Software Implementation The digital solution is an advanced software system that brings automation capabilities to drilling rigs, analogous to the different levels of automation found in automobiles. By drawing a parallel to car automation, one can better understand the system’s functionality and benefits. Level 1 consists of task automation, comparable to automatic driving features such as cruise control. In this context, the drilling software takes on the role of automating specific tasks within the drilling process. Level 2 involves process automation, similar to a car’s ability to autonomously park itself. The digital solution automates selected processes in drilling operations that enable efficient and precise execution. Level 3 encompasses adaptive automation, akin to a self-driving car that dynamically adapts to its surroundings. The drilling software continuously analyzes the real-time conditions of the wellbore and suggests optimal drilling parameters based on proprietary algorithms that leverage the well-known DeTournay model principle. The drilling software actively controls four key rig machines: the drawworks, topdrive, automated driller, and mud pumps. The solution adjusts drilling parameters intelligently, optimizing the drilling process and improving overall efficiency and drilling stability.

Human–machine collaboration for enhanced decision-making in governance

Abstract A detailed exploration is presented of the integration of human–machine collaboration in governance and policy decision-making, against the backdrop of increasing reliance on artificial intelligence (AI) and automation. This exploration focuses on the transformative potential of combining human cognitive strengths with machine computational capabilities, particularly emphasizing the varying levels of automation within this collaboration and their interaction with human cognitive biases. Central to the discussion is the concept of dual-process models, namely Type I and II thinking, and how these cognitive processes are influenced by the integration of AI systems in decision-making. An examination of the implications of these biases at different levels of automation is conducted, ranging from systems offering decision support to those operating fully autonomously. Challenges and opportunities presented by human–machine collaboration in governance are reviewed, with a focus on developing strategies to mitigate cognitive biases. Ultimately, a balanced approach to human–machine collaboration in governance is advocated, leveraging the strengths of both humans and machines while consciously addressing their respective limitations. This approach is vital for the development of governance systems that are both technologically advanced and cognitively attuned, leading to more informed and responsible decision-making.

A Systematic Review of the Effects of AI-Assisted Moderation on Individuals and Groups

This review paper provides a conceptualization of AI-assisted content moderation with various degrees of autonomy and summarizes experimental evidence for how different levels of automation in content moderation and related losses of autonomy affect individuals and groups. Our results show that current research predominantly focuses on individual level effects, necessitating a shift toward understanding the impact on groups. The study highlights gaps in exploring different levels of AI-assisted moderation interventions and misalignments of different conceptualizations that make comparing research results difficult. The discussion underscores the prevailing emphasis on harmful content removal and advocates for investigating more constructive moderation techniques, emphasizing the potential of AI in fostering normative, higher-level outcomes.

Automation of interaction—interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

AbstractInteraction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials.

Towards a novel intelligent and fully interactive IoT framework for residential buildings

Intelligent operation of buildings plays a crucial role in enhancing energy efficiency, especially in commercial buildings. However, residential buildings suffer from lack of management in energy consumption, contributing to global carbon emissions. Challenges include limited autonomous features, lack of scalability, and costly existing solutions. This paper presents a preliminary study toward the realization of a universal, scalable, and low-cost residential control framework to improve energy performance and reduce the cost of existing building retrofitting approaches. The residential system in its envisioned form may include thermostat, dimming, shading, and window-opening controls. For this study, a preliminary simulation-based investigation of a typical house in multiple climates is performed. To this end, the occupancy profiles data to layout different levels of automation of thermostats and shades have been employed, proposing 3 levels based on a variety of thermostat setpoints and shading. Data from the American Time Use Survey is utilized to inform occupancy and form the integrated automatic control strategy. This approach paves the way for building management systems in homes that integrate IoT-enabled devices for seamless operation, supporting decarbonization and creating interactive and smart buildings. The results showed that from 15% up to 70% reduction in heating and cooling loads are obtainable. This underscores the great potential of implementation of smart control systems in residential buildings.

Analysis of Australian public acceptance of fully automated vehicles by extending technology acceptance model

There has been an increasing trend in using user acceptance models to explore the public acceptance of automated vehicles (AVs) in different countries. Most of the previous studies have analysed perceived usefulness, behavioural attitude, subjective norms as well as perceived ease of use, but other important factors, such as trust and data privacy, have not been adequately considered. Likewise, public perceptions of fully AVs are limited in the literature as most studies focus on different levels of automation. This study aims to assess the behavioural intention to use fully AVs in Australia by extending the Technology Acceptance Model (TAM) that includes data privacy and trust in the TAM constructs. Based on a survey of 809 adult respondents from Australia, the model was evaluated with Structural Equation Modelling.The research revealed perceived trust and perceived data privacy is the first and second most important variable affecting the attitude, followed by perceived ease of use and usefulness. Perceived data privacy was discovered to positively impact attitude, perceived trust, perceived usefulness, perceived ease of use as well as behavioural intentions. The perceived trust mediated perceived data privacy on the attitudes in this study. Additionally, the two major variables in the proposed model – perceived trust and data privacy- affect attitudes of AVs significantly, with total effects being 0.637 and 0.604, respectively. Attitude is the most significant variable that correlates with behavioural intentions, which leads to acceptance of fully AVs. Multigroup analysis showed the gender, age and income related differences regarding the public acceptance of AVs. Several theoretical and practical implications are discussed in this paper.

HUs Fleet Management in an Automated Container Port: Assessment by a Simulation Approach

Freight fleet management (FM) can be defined as an optimization process of freight vehicles scheduling and routing, aimed at reducing time, costs, energy, and environmental impacts. In the specialized literature, there are many FM studies. The focus of this paper is on the FM in the context of a container port to increase the productivity and pursue the sustainability of the logistics node improving the performance by using freight advanced handling units (HUs). The use of automated HUs reduces the time and costs of each port activity, eliminating timewasters and increasing safety; however, it requires advanced intelligent management. Moreover, the automation is in line with energy and environmental sustainability. The paper aims to assess the impacts due to the automation of HUs by using a simulation approach. After a framework of traditional and automated HUs, allowing to highlight their main characteristics, the work considers the organizational problems of a container port and introduces a methodological approach to manage the FM of HUs. Finally, the application to a real context is presented to compare the present configuration of a container port with some project scenarios, considering different levels of automation, as the partial and total replacements of traditional HUs with advanced/automated vehicles.

Configuration and Design Schemes of Environmental Sensing and Vehicle Computing Systems for Automated Driving: A Review

The recent proliferation of sensing and computing technologies has promoted the rapid development of automated driving. A series of automated driving systems are consecutively released by manufactures in recent years. These systems at different automated driving levels rely on various perception sensors and vehicle computing system to detect the environment and process the real-time data. The configuration of environmental sensing and vehicle computing in the system design stage for facilitating automated driving are attracting more and more attention. In this paper, we investigate the state-of-the-art configuration design schemes of perception system and computing system in automated driving at different levels of automation, involving up to thirty automated driving systems or vehicles. Comprehensive analysis by statistics approaches is followed with the emphasis of underlying design patterns and the cross relationships among environmental sensing, vehicle computing and automated driving levels. Furthermore, from a general perspective, we analyze and conclude the requirement on the perception system and the computing system in automated vehicles, considering typical features in each automated driving level. We expect that this work could serve as a reference for practical engineering on the configuration design for automated driving systems, and promote academic research on relative theories and methodologies for system design.

Research on Heterogeneous Traveler Travel Mode Choices with Differences under a Mixed Traffic Environment.

Autonomous vehicles (AVs) have been made possible by advances in sensing and computing technologies. However, the high cost of AVs makes privatization take longer. Therefore, companies with autonomous vehicles can develop shared autonomous vehicle (SAV) projects. AVs with a high level of automation require high upgrade and use costs. In order to meet the needs of more customers and reduce the investment cost of the company, SAVs with different levels of automation may coexist for a long time. Faced with multiple travel modes (autonomous cars with different levels of automation, private cars, and buses), travelers' travel mode choices are worth studying. To further differentiate the types of travelers, this paper defines high-income travelers and low-income travelers. The difference between these two types of travelers is whether they have a private car. The differences in time value and willingness to pay of the two types of travelers are considered. Based on the above considerations, this paper establishes a multi-modal selection model with the goal of maximizing the total utility of all travelers and uses the imperial competition algorithm to solve it. The results show that low-income travelers are more likely to choose buses and autonomous vehicles with lower levels of automation, while high-income travelers tend to choose higher levels of automation due to their high value of travel time.

ИСПОЛЬЗОВАНИЕ ПОЛУПРОВОДНИКОВЫХ ЭЛЕМЕНТОВ ПЕЛЬТЬЕ В ГАЗОИСПОЛЬЗУЮЩЕМ ВАРОЧНОМ КОТЛЕ

Cooking boilers powered by natural gas are promising equipment for the preparation of feed in private farms. Based on the patent search, it has been established that there is a wide choice of gas-using boilers with different levels of automation of the process of thermal processing of the product and ensuring safety during operation. At the preliminary stage of the study, it was found that with a smooth control of the power of the gas burner of the digester during the preparation of feed, gas savings of up to 19% are achieved. The principle of self-regulation of gas consumption by a gas burner is determined in proportion to the heat flow consumed by the digester. A working experimental sample was made on the basis of an automation kit for a digester with smooth control of the power of a gas burner. A mathematical model has been developed that describes the processes of heat and mass transfer in a feed boiler with self-regulation of power. Based on the analysis of energy supply systems in digesters and heating boilers, the expediency of developing a new promising power self-regulation scheme based on Peltier semiconductor thermoelements has been established. As shown by the results of experimental studies of automation in various operating modes, the voltage on thermoelectric elements changes in proportion to the temperature difference. It is established that the minimum voltage is 0.07V, the maximum is 0.7V. The voltage and temperature of the product and hot steam in the jacket were measured using TEK DT 830B multimeters. It has been established that the best sensitivity of the thermal regime control system is achieved if the thermoelectric converter module is equipped with nine elements that change the output voltage in proportion to the temperature difference in the steam jacket of the digester.