Unpredictable Environments Research Articles

Making sense of body-worn cameras as a violence prevention tool for ambulance staff

Body-worn cameras (BWCs) were introduced for ambulance crews in England in 2021 in response to rising rates of abuse targeted towards ambulance crews by members of the public. As part of an evaluation of the effectiveness of BWCs as a tool for reducing occupational violence, we produced a non-systematic literature review to examine the real-world outcomes and cost-effectiveness of BWCs in a variety of settings and countries. Our review identified minimal literature on the topic pertaining to the ambulance sector. While there is considerable evidence relating to BWCs and violence prevention more generally, much of it is poor quality, anecdotal, and focused on specific professions with limited scope for making generalisations. There is some evidence that BWCs have the potential to address violence and abuse directed towards ambulance staff. However, our analysis of costs and benefits also raises questions about evidence concerning the value of BWCs and their potential negative impacts on staff wellbeing. The literature is unclear about the underlying mechanisms through which BWCs might support violence reduction, and it is likely that any benefits will be mediated by the complex and unpredictable environment in which BWCs are being used, which is likely to have strong independent effects on outcomes. The more that BWC research is able to explore such mechanisms in detail to inform our understanding of how and under what conditions specific outcomes are observed, the more we will be able to make sense of the variable findings produced in this area to date.

Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities

Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.

Impact of Compensation Packages on Employees Job Satisfaction: Moderating Role of Environmental Uncertainty in Banking Institutions of Kabul

This study investigates the impact of compensation on employees' job satisfaction in private banks located in Kabul, Afghanistan. Additionally, it explores the moderating role of environmental uncertainty in this relationship. The study aims to determine the extent to which compensation packages, affects employee job satisfaction, while considering the influence of environmental uncertainty as a moderating variable. Cross-sectional quantitative data is collected through an adopted self-administered survey questionnaire from employees (N=660) of private banks in Kabul. Statistical analyses, including descriptive analysis, reliability analysis, correlation analysis, ANOVA, and regression analysis were conducted using the SPSS. The findings of this study indicate that compensation packages have a significant impact on employee job satisfaction. It reveals that employees' perception of their compensation, including salary, benefits, and incentives, plays a crucial role in determining their level of job satisfaction. Higher levels of compensation are positively associated with increased job satisfaction among employees in private banks. Furthermore, the study highlights the moderating role of environmental uncertainty in the relationship between compensation packages and job satisfaction. Environmental uncertainty refers to the volatility, complexity, and unpredictability of the external business environment. The findings suggest that organizations should carefully design and implement compensation packages that align with employees' needs and expectations, considering the contextual factors of the business environment.

Foot trajectory as a key factor for diverse gait patterns in quadruped robot locomotion

Four-legged robots are becoming increasingly pivotal in navigating challenging environments, such as construction sites and disaster zones. While substantial progress in robotic mobility has been achieved using reinforcement learning techniques, quadruped animals exhibit superior agility by employing fundamentally different strategies. Bio-inspired controllers have been developed to replicate and understand biological locomotion strategies. However, a comprehensive understanding of the influence of foot trajectories on gait patterns is still necessary. This study provides a groundbreaking perspective on the essential impact of these trajectory shapes on robotic gait patterns and overall performance. By employing the Unitree A1 robot model with a bio-inspired neural control system, our simulations demonstrate that specific trajectory shapes effectively replicate diverse and natural gait patterns, such as trotting, pacing, and galloping, thereby improving adaptability to diverse terrains. Specifically, trajectories designed for pacing exhibit superior performance on rough terrain, excelling in efficiency and adaptability over other gaits. This study highlights the significance of foot trajectory in augmenting robotic locomotion and establishes a new benchmark for developing advanced robots that operate effectively in unpredictable environments.

Single-Atom Suture.

In atomically thin two-dimensional (2D) materials, grain boundaries (GBs) are ubiquitous, displaying a profound effect on the electronic structure of the host lattice. The random configuration of atoms within GBs introduces an arbitrary and unpredictable local electronic environment, which may hazard electron transport. Herein, by utilizing the Pt single-atom chains with an ultimate one-dimensional (1D) feature (width of a single atom and length up to tens of nanometers), we realized the suture of the electron pathway at GBs of diversified transition metal dichalcogenides (TMDCs). Theoretical calculations reveal that the construction of Pt single-atom sutures (SAS) prompts the emergence of electronic states proximal to the Fermi level, effectively modulating the transformation of the electronic structure from semiconductivity to metallicity. This transformation underscores the pivotal role of Pt SAS in reconfiguring the electron pathway. Benefiting from this, the Pt SAS-MoS2 emerges as an excellent catalyst, exhibiting an overpotential of 41 mV at 10 mA cm-2 and a Tafel slope of 54 mV dec-1 in hydrogen evolution reaction. Our results offer an understanding of the electron conduction pathway contributed by ultraordered atomic arrangement and the innovative mechanisms for future potential catalysts with an optimized architecture.

Human-Inspired Gait and Jumping Motion Generation for Bipedal Robots Using Model Predictive Control.

In recent years, humanoid robot technology has been developing rapidly due to the need for robots to collaborate with humans or replace them in various tasks, requiring them to operate in complex human environments and placing high demands on their mobility. Developing humanoid robots with human-like walking and hopping abilities has become a key research focus, as these capabilities enable robots to move and perform tasks more efficiently in diverse and unpredictable environments, with significant applications in daily life, industrial operations, and disaster rescue. Currently, methods based on hybrid zero dynamics and reinforcement learning have been employed to enhance the walking and hopping capabilities of humanoid robots; however, model predictive control (MPC) presents two significant advantages: it can adapt to more complex task requirements and environmental conditions, and it allows for various walking and hopping patterns without extensive training and redesign. The objective of this study is to develop a bipedal robot controller using shooting method-based MPC to achieve human-like walking and hopping abilities, aiming to address the limitations of the existing methods and provide a new approach to enhancing robot mobility.

Sustainable regional development from the perspective of economic resilience: Based on the impact of COVID-19.

The process of regional economic development is marked by a sustained exposure to external disturbances. In today's unpredictable and tumultuous global environment, such disturbances have become increasingly common, underlining the need to advance a region's economic resilience and foster adaptive mechanisms to handle environmental flux. Comparing the typical provinces in eastern, central, western and northeastern regions during the COVID-19 epidemic period, it found that the economic resilience performance of Henan Province, which is a representative of the central region, has the following characteristics. Henan Province has the lowest total production resilience in the selected regions, but it is in the second position in terms of development potential resistance, while it is in the middle range in terms of resilience performance, and it has a weaker resistance performance in terms of the resilience indicator of the standard of living of the residents. Against this backdrop, this study undertakes an analysis and comparison of the economic resilience indicators of the 18 cities in Henan Province to suggest several refinements. It urges a promotion of the integrated development of the primary, secondary, and tertiary industries through increasing emphasis on the industrial sector, which will enable a transformation of the sectoral structuring. Further, digitization can help the upgrading of industries' intelligence and function as a lever to raise their competitiveness. Innovation in enterprises is also crucial in enhancing industrial competitiveness, by bolstering the technological innovation capabilities of enterprises and simultaneously boosting the wage-based incomes of residents, resulting in an overall improvement in their standard of living. Also, the study emphasizes the establishment of a digital talent platform to construct a sound regional talent ecology system.

코칭행동이 혁신행동에 미치는 영향: 자기결정성과 조직몰입의 매개효과 조직지원인식의 조절효과 중심으로

In an environment threatened by rapid environmental changes in 4th industrial era technology, global economic downturn and instability, and the acceleration of technological change, companies are desperate to survive and secure competitiveness. Competition between industries is intensifying and requires the continuous change and innovation of companies in unpredictable environments. In uncertain environments, the need for coaching behaviors that can induce innovative behavior is expanding as companies demand creativity and challenges taken on by organization members. This study verified the influential relationship between the coaching behavior of leaders and the innovation behavior of members, and attempted to confirm the mediating effects of the variables of organizational commitment and self-determination from the intrinsic motivation-level of the individual along with moderating effects through perceived organizational support. This study showed that factors of coaching behavior can have a practical effect on the leaders of organizations who reinforce innovation. Results of this study showed that, first, the coaching behavior of leaders was verified to have a positive (+) effect on the innovation behavior, self-determination, and organizational commitment of members. Second, the coaching behavior of leaders was shown to have a positive (+) effect relationship on the mediating effects of self-determination and organizational commitment, among the innovation behavior of members. Third, perceived organizational support was found to have a moderating effect between the coaching behavior of leaders and the innovation behavior of members. This study can strengthen and differentiate theoretical foundations that are relatively under-researched in the coaching field through self-determination theory and it can be a meaningful study that expands academic mechanisms. In practice, the coaching behavior of leaders can promote corporate sustainability and the innovation behavior of members seeking a competitive advantage and it can provide an empirical basis for the coaching behavior development of leaders.

Neural network model for autonomous navigation of a water drone

Water drones have significant potential for use in environmental monitoring, search and rescue operations, and marine infrastructure inspection, but the specific conditions of the water environment make it difficult to implement stable autonomous navigation. The object of research presented in this paper is the machine learning process for autonomous navigation of a water drone model in a simulated water environment. The purpose of the study is to implement a neural network model for autonomous navigation of a water drone using a reinforcement learning method that provides improved obstacle avoidance and adaptation to water currents. To achieve this purpose, a new neural network model for autonomous drone navigation in the water environment based on the reinforcement learning method is proposed, which differs from the existing ones in that it uses an improved drone control algorithm that takes into account the speed and direction of the water current, which makes it possible to stabilize the process of generating neural network coefficients. To ensure an effective learning process and optimization of the model, a simulation training environment was developed using the USVSim simulator, which contains various factors that interfere with the drone's movement, such as water current and the presence of other objects. The water drone, acting as an agent, gradually learns to choose the most effective actions to maximize positive rewards through trial and error, interacting with the environment and adapting to changing conditions. This process takes place through the use of a Deep Q-Network: the drone provides the value of its current state to a deep neural network; the neural network processes the data, predicts the value of the most effective action, and gives it to the agent. The current state of the drone is information in the form of a set of sensor readings measuring the distance to the nearest obstacles, drone’s heading and current distance to goal. The value of the effective action received from the neural network is converted into a command for the rudder that the drone can understand. The value of the drone's thruster power is calculated by separate formulas using trigonometric functions. The results of the study showed that the use of the proposed model allows the drone to make decisions in a dynamic water environment when rapid adaptation to changes is required. The model successfully adapted its strategy based on feedback from the environment, so it can be concluded that the implemented model shows significant potential for further research and applications in the field of autonomous water drones, especially in changing and unpredictable environments.

U.S. Strategic Posture Commission and the future of nuclear deterrence: Rising defenses, cognitive complexity

The United States Strategic Posture Commission identified a number of the more important challenges to American security over the next several decades. These challenges include the viability of the modernization plans for the strategic nuclear deterrent in view of the threat posed by two nuclear peer competitors, Russia and China, that is all but certain to emerge in the 2030s. As well, developing technologies for hypersonic weapons, antimissile and integrated air and missile defenses (IAMD), and cyber and space weapons are certain to create a more complex and unpredictable environment for deterrence than hitherto. The preferred mix of strategic and nonstrategic nuclear weapons for the United States required to fulfill the challenges of tailored deterrence and flexible response, going forward, will challenge the ingenuity of defense planners and budgetary managers.

Enhancing skeleton-based action recognition with hybrid real and gan-generated datasets

This research addresses the critical challenge of recognizing mutual actions involving multiple individuals, an important task for applications such as video surveillance, human-computer interaction, autonomous systems, and behavioral analysis. Identifying these actions from 3D skeleton motion sequences poses significant challenges due to the necessity of accurately capturing intricate spatial and temporal patterns in diverse, dynamic, and often unpredictable environments. To tackle this, a robust neural network framework was developed that combines Convolutional Neural Networks (CNNs) for efficient spatial feature extraction with Long Short-Term Memory (LSTM) networks to model temporal dependencies over extended sequences. A distinguishing feature of this study is the creation of a hybrid dataset that which combines real-world skeleton motion data with synthetically generated samples, produced using Generative Adversarial Networks (GANs). This dataset enriches variability, enhances generalization, and mitigates data scarcity challenges. Experimental findings across three different network architectures demonstrate that our method significantly enhances recognition accuracy, mainly due to the integration of CNNs and LSTMs alongside the broadened dataset. Our approach successfully identifies complex interactions and ensures consistent performance across different perspectives and environmental conditions. The improved reliability in recognition indicates that this framework can be effectively utilized in practical applications such as security systems, crowd monitoring, and other areas where precise detection of mutual actions is critical, particularly in real-time and dynamic environments

Assessing the Performance of ChatGPT on Dentistry Specialization Exam Questions: A Comparative Study with DUS Examinees

Aim: This study aims to evaluate the performance of the ChatGPT-4.0 model in answering questions from the Turkish Dentistry Specialization Exam (DUS), comparing it with the performance of DUS examinees and exploring the model’s clinical reasoning capabilities and its potential educational value in dental training. The objective is to identify the strengths and limitations of ChatGPT when tasked with responding to questions typically presented in this critical examination for dental professionals. Material and Method: The study analyzed DUS questions from the years 2012 to 2017, focusing on the basic medical sciences and clinical sciences sections. ChatGPT's responses to these questions were compared with the average scores of DUS examinees, who had previously taken the exam. A statistical analysis was performed to assess the significance of the differences in performance between ChatGPT and the human examinees. Results: ChatGPT significantly outperformed DUS examinees in both the basic medical sciences and clinical sciences sections across all years analyzed. The statistical analysis revealed that the differences in performance between ChatGPT and DUS examinees were statistically significant, with ChatGPT demonstrating superior accuracy in all years. Conclusion: ChatGPT’s performance on the DUS demonstrates its potential as a supplementary tool for dental education and exam preparation. However, future research should focus on integrating AI into practical dental training, particularly in assessing its real-world applicability. The limitations of AI in replicating hands-on clinical decision-making in unpredictable environments must also be considered.

A decade of genetic makeup in the aerial seed bank of a fire-evader tree

BackgroundAerial seed banks occur in plants that postpone the release of viable seeds, thereby providing a mechanism for population maintenance in unpredictable environments. Seeds retained on the mother trees offer a reservoir of genetic diversity across years, but the pattern and evolutionary consequences of such accumulated diversity are poorly understood. We characterize the genetic layout of an aerial seed bank composed of serotinous cones formed in 2003 and in 2014 in a historically fire-prone area. Our hypothesis is that within this timeframe, recurrent fires could have modified the genetic makeup of the seed bank.ResultsOur results did not reveal any genetic difference along the 12-year period, but indicated a significant genetic variation (13%) of seed banks among trees within the same year. Moreover, progeny from the seed bank showed a 4% increase of observed heterozygosity, together with 14 new alleles, compared to mother trees. Finally, a strong homogenization effect of pollen flow on the genetic diversity was detected, with a very high effective number of fathers in 2003 and 2014.ConclusionsOur results highlight the temporal stability of the genetic makeup of aerial seed banks in this fire-prone area. The maternal genetic differences and an increase of genetic diversity (linked to effective pollen flow) in new generations compared to their maternal progenitors are also noteworthy. Together, these characteristics represent key assets for serotinous forest adaptation and persistence in the face of increasing wildfire activity.

Environmental drivers of seed persistence and seedling trait variation in two Neltuma species (Fabaceae)

Abstract The endemic tree Neltuma caldenia Burk. and the shrub Neltuma flexuosa var depressa F.A. Roig (Fabaceae; subfam: Mimosoideae) are two promising species from the central region of Argentina, with high potential for use in the restoration of disturbed environments, for extensive livestock grazing and apiculture. Both species have seeds with physical dormancy. Ecological study of native species is important from the point of view of rehabilitation of degraded areas by natural regeneration or via seed-based programmes. The objective of this study was to evaluate soil seed bank persistence and seedling traits to understand variation among different populations of each of the study species growing along an ecological gradient and to identify potential components driving this variation. Home environments influenced seed bank persistence, which was higher in populations originating from more arid and unpredictable environments where it could act as a bet-hedging strategy between years and seasons. We also observed differences associated with seedling growth traits between the species and populations. Populations with higher seed persistence were associated with greater seedling growth in N. caldenia. The rapid elongation rate of N. caldenia seedlings growing in large populations and unstable environments could help their ability to escape drought by accessing deeper soil moisture and would confer a high relative competitive ability. In contrast, N. f. var depressa displayed higher seed persistence, which was associated with more arid and unstable conditions and correlated with lower seedling growth, possibly due to an inbreeding depression effect, resulting from the presence of mother plants in low-densities or being isolated from other individuals. There was no effect of population size on seed persistence. To better understand seed persistence and associated seedling trait variation, future studies need to consider the genotype, environment and landscape conditions.

A Comprehensive Study of Recent Path-Planning Techniques in Dynamic Environments for Autonomous Robots.

This paper presents a comprehensive review of path planning in dynamic environments. This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. The review discusses the key trends, challenges, and gaps in current methods to emphasize the need for more efficient and robust algorithms that can handle complex and unpredictable dynamic environments. Moreover, it discusses the importance of collaborative decision making and communication between robots to optimize path planning in dynamic scenarios. This work serves as a valuable resource for advancing research and practical applications in dynamic obstacle navigation.

Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction

The limited coverage of terrestrial base stations and the limited transmission distance and onboard resources of satellite communications make it difficult to ensure the quality of communication services for marine users by relying only on satellites and terrestrial base stations. In contrast, UAVs, as flexible mobile communication nodes, have the capacity for dynamic deployment and real-time adjustment. They can effectively make up for the communication blind spots of traditional satellites and ground base stations in the marine environment, especially in the vast and unpredictable marine environment. Considering the mobility of maritime users, one can effectively reduce the communication delay and optimize the deployment scheme of UAVs by predicting their sailing trajectories in advance, thus enhancing the communication service quality. Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). It aims to optimize the coverage efficiency of the maritime communication network, minimize the communication delay, and effectively reduce the energy consumption of UAVs. In this algorithm, the RUDD algorithm employs a modified Long Short-Term Memory (LSTM) network to predict the maritime user’s trajectory, utilizing its strengths in processing time-series data to provide accurate predictions. The prediction results are then used to guide the Proximal Policy Optimization (PPO) algorithm for the dynamic deployment of UAVs. The PPO algorithm can optimize the deployment strategy in dynamic environments, improve communication coverage, and reduce energy consumption. Simulation results show that the proposed algorithm can complement the existing satellite and terrestrial networks well in terms of coverage, with a communication coverage rate of more than 95%, which significantly improves the communication quality of marine users in areas far from land and beyond the reach of traditional networks, and enhances network reliability and user experience.

Research on Real-Time Multi-Robot Task Allocation Method Based on Monte Carlo Tree Search

Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of robot operations. Traditional static task allocation strategies often struggle to meet the efficiency and responsiveness demands of these complex settings, while optimization heuristics, though improving planning time, exhibit limited scalability. To address these limitations, this paper proposes a task allocation method based on the Monte Carlo Tree Search (MCTS) algorithm, which leverages the anytime property of MCTS to achieve a balance between fast response and continuous optimization. Firstly, the centralized adaptive MCTS algorithm generates preliminary solutions and monitors the state of the robots in minimal time. It utilizes dynamic Upper Confidence Bounds for Trees (UCT) values to accommodate varying task dimensions, outperforming the heuristic Multi-Robot Goal Assignment (MRGA) method in both planning time and overall task completion time. Furthermore, the parallelized distributed MCTS algorithm reduces algorithmic complexity and enhances computational efficiency through importance sampling and parallel processing. Experimental results demonstrate that the proposed method significantly reduces computation time while maintaining task allocation performance, decreasing the variance of planning results and improving algorithmic stability. Our approach enables more flexible and efficient task allocation in dynamically evolving and complex environments, providing robust support for the deployment of multi-robot systems.

Knowledge Management and its Effect on Firms’ Competitive Strategy and Efficiency in the Service Sectors of the Nigeria Economy

There is an amplified interest in an organisation’s competitive strategy and efficiency in performing its functions. Knowledge management (KM) thus, serves as a means of improving an organisation’s efficiency. However, it has become increasingly apparent that a lack of knowledge management and resilience in a dynamic and unpredictable corporate environment is a severe business concern that reduces competitive advantage and a firm’s performance. This research, therefore, explored knowledge management and its effect on firms’ competitive strategy and efficiency in the service sectors of the Nigerian economy to bridge this gap. We used an exploratory study technique, administering a self-designed questionnaire to 386 employees and managers from chosen service organisations. The questionnaire received 333 valid responses, representing 90.5% of the total. A multiple regression approach is used to analyze the information. The research results supported the hypothesis that KM engenders firms’ competitive strategy and efficiency through robust reward systems. The results also showed that if employees are satisfied and their interests are safeguarded, acquiring knowledge, preservation, distribution, and utilisation would be easier, which leads to long-term organisational profitability and performance. Organisations should thus link knowledge management with employee interests and build broad strategies while fostering an atmosphere conducive to KM development and utilisation.

Engaging leaders, proactive followers: engaging leadership and followers’ job crafting, performance and intrapreneurship

PurposeIn the current economy, organizations operate in an unpredictable and changing environment in which employees’ performance and proactivity can represent strategic advantages. However, it is not clear how managers can stimulate the performance and proactivity of subordinates. The purpose of the current study was to investigate the relationships between engaging leadership (an approach to managing people that is focused on fulfilling followers’ psychological needs for autonomy, competence, relatedness and meaningfulness) and subordinates’ job performance and intrapreneurship (organizational venture creation and the strategic renewal generated by employees within the company they work for). Based on the self-determination theory and job demands-resources model, we expected followers’ work engagement and job crafting (changes regarding job resources and demands that employees proactively make) to mediate the link between engaging leadership and followers’ performance and intrapreneurship.Design/methodology/approachA sample of 401 employees completed the study questionnaire. Employees evaluated their direct supervisors’ engaging leadership and reported on their own work engagement, job crafting, performance and intrapreneurship. The proposed relationships were tested using SEM.FindingsResults indicated that the relationships between engaging leadership and followers’ job performance and intrapreneurship were mediated by followers’ work engagement and two job crafting components (increasing structural resources and increasing challenging demands).Originality/valueThis study expands the range of outcomes of engaging leadership. In addition, it provides an explanatory mechanism for the relationship between engaging leadership and followers’ proactivity.

How different levels of semantic segmentation affect human perception of driving scenes

As automated vehicles continue to advance, teleoperation has emerged as a critical support system for navigating complex and unpredictable environments that exceed the vehicles' current autonomous capabilities. A main issue in the implementation of teleoperation is latency caused by the high bandwidth required to transmit the video feed from the vehicle to the remote teleoperation station. A possible approach for addressing the latency problem is the transfer of lower-resolution or compressed videos between the vehicle and the teleoperation station. When applying semantic segmentation on the video feed, many pixels are mapped to a limited set of possible colors according to the types of objects that they represent. This concept has been commonly used in autonomous driving algorithms and has the potential to enable the transferring of smaller-sized videos thus reducing bandwidth. In this study, we examine how presenting semantically segmented driving scenes to humans affects their perception of the scene, and specifically, how it affects their hazard perception and situation awareness. We conducted two user studies comparing the effects of using different levels and types of semantic segmentation. Our results indicate that viewing partly segmented scenes, such that only a selected set of object types are colored, commonly achieves the same effect, and sometimes even outperforms a realistic view. Our study and its insights may pave the way for future research, development, and design of teleoperation systems of automated vehicles.