Pilots’ Willingness to Operate in Urban Air Mobility Integrated Airspace: A Moderated Mediation Analysis

Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by NASA, FAA, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on-board compared to remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated operations.

A labelling system and automation comparison index for industry 4.0 system

Purpose This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level. Design/methodology/approach Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps. Findings The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making. Research limitations/implications Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work. Practical implications The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly. Originality/value To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

Adaptable (Not Adaptive) Automation: The Forefront of Human–Automation Teaming

Objective Identify a critical research gap for the human factors community that has implications for successful human–automation teaming. Background There are a variety of approaches for applying automation in systems. Flexible application of automation such that its level and/or type changes during system operations has been shown to enhance human–automation system performance. Method This mini-review describes flexible automation in which the level of automated support varies across tasks during system operation, rather than remaining fixed. Two types distinguish the locus of authority to change automation level: adaptable automation (the human operator assigns how automation is applied) has been found to aid human’s situation awareness and provide more perceived control versus adaptive automation (the system assigns automation level) that may impose less workload and attentional demands by automatically adjusting levels in response to changes in one or more states of the human, task, environment, and so on. Results In contrast to vast investments in adaptive automation approaches, limited research has been devoted to adaptable automation. Experiments directly comparing adaptable and adaptive automation are particularly scant. These few studies show that adaptable automation was not only preferred over adaptive automation, but it also resulted in improved task performance and, notably, less perceived workload. Conclusion Systematic research examining adaptable automation is overdue, including hybrid approaches with adaptive automation. Specific recommendations for further research are provided. Application Adaptable automation together with effective human-factored interface designs to establish working agreements are key to enabling human–automation teaming in future complex systems.

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capabilities, communication systems, and the information systems of the manufacturing systems which is reflected in the concept of Industry 4.0. Furthermore, the robotics systems are continuously required to address new challenges in the industrial and manufacturing domain, like keeping humans in the loop, among other challenges. Briefly, the keeping humans in the loop concept focuses on closing the gap between humans and machines by introducing a safe and trustworthy environment for the human workers to work side by side with robots and machines. It aims at increasing the engagement of the human as the automation level increases rather than replacing the human, which can be nearly impossible in some applications. Consequently, the collaborative robots (Cobots) have been created to allow physical interaction with the human worker. However, these cobots still lack of recognizing the human emotional state. In this regard, this paper presents an approach for adapting cobot parameters to the emotional state of the human worker. The approach utilizes the Electroencephalography (EEG) technology for digitizing and understanding the human emotional state. Afterwards, the parameters of the cobot are instantly adjusted to keep the human emotional state in a desirable range which increases the confidence and the trust between the human and the cobot. In addition, the paper includes a review on technologies and methods for emotional sensing and recognition. Finally, this approach is tested on an ABB YuMi cobot with commercially available EEG headset.

Sight distance analyses for autonomous vehicles in Civil 3D

Nowadays, self-driving cars have a wide reputation among people that is constantly increasing, many manufacturers are developing their own autonomous vehicles. These vehicles are equipped with various sensors that are placed at several points in the car. These sensors provide information to control the vehicle (partially or completely, depending on the automation level). Sight distances on roads are defined according to various traffic situations (stopping, overtaking, crossing, etc.). Safety reasons require these sight distances, which are calculated from human factors (e.g., reaction time), vehicle characteristics (e.g., eye position, brakes), road surface properties, and other factors. Autodesk Civil 3D is a widely used tool in the field of road design, the software however was developed based on the characteristics of the human drivers and conventional vehicles.

Organizational Culture of Russian Companies: State, Problems and Features of Transformation

The purpose of this article is to establish the relation between the level of employee’s satisfaction and the achieved digitalization level of the company. At the same time, job satisfaction is considered as an important factor in the formation and development of corporate culture. The authors analyze a problem of decreasing the level of job satisfaction in the context of internal organizational changes occurring in the company as part of its digital transformation. The problem is that the creative component and motivational attitudes of employees decrease with the increasing use of modern information technology tools. In the context of digitalization, the development of an effective strategy for interaction between the company's top management and its employees is largely subject to the following chain of criteria: the automation level of business processes – the degree of satisfaction with working conditions by the staff – in the corporate culture of the company.

A Vehicle Guidance Model with a Close-to-Reality Driver Model and Different Levels of Vehicle Automation

This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: the first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately.