Different Levels Of Automation Research Articles

Influence of Lean Automation on Resource Wastage in Sugar Industries: Case in Kenya

Sustainability of resource utilization in local sugar industries is on a decline due to the adoption of obsolete technology, among other factors. In this regard, there is a need for an advanced manufacturing approach to address resource wastages in the local industries. Resource utilization efficiency of a sugar cane industry can be varied through combinations of energy consumption, setup time, and cycle time. However, this depends on manufacturing technology used by the industry that is embodied in different levels of automation. Thus, this study carried out an analysis of the different levels of automation at different stages of the pre-milling process of sugarcane in the case industry to determine the optimum automation level for efficient resource utilization. A randomized block experiment was adopted, and for each indicator under investigation, seven replicates were conducted for the reliability of results. It was found that the rate of power consumption employing LoA 4 (conventional automation) was relatively higher with a total of 45044 kW compared to when LoA 5 (SCADA) or LoA 6 (DCS) was used with a total power consumption of 42058 kW and 42008 kW respectively. Similarly, LoA 5 and 6 have virtually negligible setup and cycle times involved except when it is after a general plant overhaul. This is as a result of minimum variations in the process parameters due to their real-time monitoring and control. Therefore, lean automation proves to be the ultimate technique that should be adopted in our sugar industries.

Attribution of blame of crash causation across varying levels of vehicle automation

Whilst public opinion towards automated vehicles is largely favourable, there are recurrent concerns around responsibility in the event of a crash (Kyriakidis et al., 2015). There is debate about the complexities regarding the legal responsibility of crashes involving automated vehicles, with opinions ranging from the driver is always accountable to the impossibility of holding an ‘automated driver’ responsible. Whilst “who is responsible” for a crash at different levels of vehicle automation has been debated, little is known about public opinion around the attribution of blame in automated vehicle crashes. In order to better understand how these might impact trust and adoption of these technologies, the present study aimed to understand public perceptions of responsibility for crashes involving different levels of automation, and the perceived consequences of that responsibility. A total of 129 undergraduate students, aged between 19 and 61 (M = 24.6, SD = 7.64) read four vignettes which detailed a pedestrian crash scenario with level of vehicle automation being manipulated in each vignette (manual driving, partially automated, highly automated and fully automated driving). Participants were asked three open-ended questions; ‘Where do you assign blame?’, ‘Based on where you assign blame, what course of action would you take from here?’, and ‘How could this event be prevented in the future?’. Results revealed that participants attributed blame to six stakeholder categories (driver, pedestrian, car, government, manufacturer and programmer). As automation increased, the proportion of participants who blamed the driver decreased, whilst those blaming the manufacturer increased. Participants commonly identified legal action against the driver, the manufacturer or both as their course of action. The proportions varied across level of automation, with legal action against the ‘driver’ still identified when the vehicle was fully automated. Furthermore, as level of automation increased, there were increased calls for automation to be reviewed, improved or completely avoided. Overwhelmingly the findings from this study highlight that the public believe that the ultimate responsibility for a crash is in the hands of the human driver, rather than the ‘automated driver’. These findings have implications for the public trust and rates of adoption of automated vehicles. Further it highlights the need for greater governance and legal frameworks around the outcomes of crashes involving automated vehicles.

Editorial for Special Issue: Test and Evaluation Methods for Human-Machine Interfaces of Automated Vehicles

Today, OEMs and suppliers can rely on commonly agreed and standardized test and evaluation methods for in-vehicle human–machine interfaces (HMIs). These have traditionally focused on the context of manually driven vehicles and put the evaluation of minimizing distraction effects and enhancing usability at their core (e.g., AAM guidelines or NHTSA visual-manual distraction guidelines). However, advances in automated driving systems (ADS) have already begun to change the driver’s role from actively driving the vehicle to monitoring the driving situation and being ready to intervene in partially automated driving (SAE L2). Higher levels of vehicle automation will likely only require the driver to act as a fallback ready user in case of system limits and malfunctions (SAE L3) or could even act without any fallback within their operational design domain (SAE L4). During the same trip, different levels of automation might be available to the driver (e.g., L2 in urban environments, L3 on highways). These developments require new test and evaluation methods for ADS, as available test methods cannot be easily transferred and adapted. The shift towards higher levels of vehicle automation has also moved the discussion towards the interaction between automated and non-automated road users using exterior HMIs. This Special Issue includes theoretical papers a well as empirical studies that deal with these new challenges by proposing new and innovative test methods in the evaluation of ADS HMIs in different areas.

Self Driving Cars: The Road Ahead

This paper focuses on overview of self-driving cars and its current scenario in India as well as globally. An Autonomous vehicle is not a new concept to the world but it has gained more popularity in last two decades. Self-driving cars work on basic three steps, perception, decision making and control. This paper explains different levels of automation in self-driving cars along with basic perception components and sensors which an autonomous vehicle needs to identify the surrounding. The paper also briefs about history and discuss the challenges to implement self-driving cars on road. Further the paper discusses the upcoming areas in which budding researchers in India can work to achieve up to third level of automation in self-driving cars.

Automation of air-conditioning systems with controlled microclimate in the Russian penitentiary system

The global development of information technology is increasingly integrated into all spheres of human life and is no exception to the technological field in which one of the most promising is the development of air conditioning systems with controlled microclimate. This study discusses some aspects of climate control using programmable logic microcontrollers. This study improves the efficiency of the use of air traffic control systems in the penitentiary system of the Russian Federation through the use of modern achievements of science and technology. The article presents two options for using different levels of automation in climate control systems in closed rooms of the Russian penitentiary system, depending on the volume of production, the algorithms of the climate control system are considered.

Povećana sigurnost radnika primjenom kolaborativnih robota u proizvodnim procesima Industrije 4.0

SUMMARY: By applying Industry 4.0, modernization of the production processes in industry is achieved. However, the safety of workers must be a priority. Automation of production processes and raising it to a higher level can be achieved by employing collaborative robots working together with workers. The degree of safety measures guarantees that there are no work injuries. In using collaborative robots we exploit all the advantages that they possess over first-generation industrial robots. They work together with workers, workers work in a safe environment, robots take up less space, they are not physically separated from workers, they are easy to manipulate, they are cheaper, and are suitable for small and medium size companies. We have the possibility of introducing different levels of automation in the production process, i.e. we can partially automate the tasks where complete automation is too complex or not economical. The use of collaborative robots will grow in the future, since the goals of the fourth industrial revolution cannot be achieved without collaborative robots, in other words, without the "smart manufacturing process" or "smart factory".

Quantifying impacts of automation on resilience of distribution systems

Automating the process of restoring service to customers after a large-scale outage event have significant impacts on the agility and speed of recovery in distribution systems. This study develops a set of probabilistic metrics to assess the impact of automation in enhancing the resilience of power distribution systems. The proposed metrics capture the features and detailed process of automatically locating and isolating faults and restoring the service to customers in distribution systems. In addition, this study develops a model to evaluate the spatio–temporal impacts of hurricane on power distribution systems, which is used to generate hurricane-induced outage scenarios to calculate the resilience metrics given different automation schemes. The proposed model is utilised to evaluate the resilience of bus number four of Roy Billinton Test System in the face of a passing hurricane. The metrics are calculated to evaluate the impact of different levels of automation throughout the network, the intensity of the hurricane, and line hardening on the resilience of the system.

The Effects of Vehicle Level Of Automation and Warning Type on Responses to Vehicle Hacking

Modern surface transportation vehicles often include different levels of automation. Higher automation levels have the potential to impact surface transportation in unforeseen ways. For example, connected vehicles with higher levels of automation are at a higher risk for hacking attempts, because automated driving assistance systems often rely on onboard sensors and internet connectivity (Amoozadeh et al., 2015). As the automation level of vehicle control rises, it is necessary to examine the effect different levels of automation have on the driver-vehicle interactions. While research into the effect of automation level on driver-vehicle interactions is growing, research into how automation level affects driver’s responses to vehicle hacking attempts is very limited. In addition, auditory warnings have been shown to effectively attract a driver’s attention while performing a driving task, which is often visually demanding (Baldwin, 2011; Petermeijer, Doubek, & de Winter, 2017). An auditory warning can be either speech-based containing sematic information (e.g., “car in blind spot”) or non-sematic (e.g., a tone, or an earcon), which can influence driver behaviors differently (Sabic, Mishler, Chen, & Hu, 2017). The purpose of the current study was to examine the effect of level of automation and warning type on driver responses to novel critical events, using vehicle hacking attempts as a concrete example, in a driving simulator. The current study compared how level of automation (manual vs. automated) and warning type (non-semantic vs. semantic) affected drivers’ responses to a vehicle hacking attempt using time to collision (TTC) values, maximum steering wheel angle, number of successful responses, and other measures of response. A full factorial between-subjects design with the two factors made four conditions (Manual Semantic, Manual Non-Semantic, Automated Semantic, and Automated Non-Semantic). Seventy-two participants recruited using SONA ( odupsychology.sona-systems.com ) completed two simulated drives to school in a driving simulator. The first drive ended with the participant safely arriving at school. A two-second warning was presented to the participants three quarters of the way through the second drive and was immediately followed by a simulated vehicle hacking attempt. The warning either stated “Danger, hacking attempt incoming” in the semantic conditions or was a 500 Hz sine tone in the non-semantic conditions. The hacking attempt lasted five seconds before simulating a crash into a vehicle and ending the simulation if no intervention by the driver occurred. Our results revealed no significant effect of level of automation or warning type on TTC or successful response rate. However, there was a significant effect of level of automation on maximum steering wheel angle. This is a measure of response quality (Shen & Neyens, 2017), such that manual drivers had safer responses to the hacking attempt with smaller maximum steering wheel angles. In addition, an effect of warning type that approached significance was also found for maximum steering wheel angle such that participants who received a semantic warning had more severe and dangerous responses to the hacking attempt. The TTC and successful response results from the current experiment do not match those in the previous literature. The null results were potentially due to the warning implementation time and the complexity of the vehicle hacking attempt. In contrast, the maximum steering wheel angle results indicated that level of automation and warning type affected the safety and severity of the participants’ responses to the vehicle hacking attempt. This suggests that both factors may influence responses to hacking attempts in some capacity. Further research will be required to determine if level of automation and warning type affect participants ability to safely respond to vehicle hacking attempts. Acknowledgments. We are grateful to Scott Mishler for his assistance with STISIM programming and Faye Wakefield, Hannah Smith, and Pettie Perkins for their assistance in data collection.

A responsibility-centered approach to defining levels of automation

Several authors and organisations have over the last 40 years suggested various ways to describe different levels of automation. In this paper we give an introduction to some of the most relevant models and present some of the challenges faced when using them. Furthermore, we suggest a new definition for autonomy, based on responsibility instead of functionality. The paper analyses some relevant cases based on the suggested definition of autonomy, and points towards some issues that should be investigated in the maritime domain related to current definitions for autonomous ships.

Driving to a future without accidents? Connected automated vehicles’ impact on accident frequency and motor insurance risk

Road traffic accidents are largely driven by human error; therefore, the development of connected automated vehicles (CAV) is expected to significantly reduce accident risk. However, these changes are by no means proven and linear as different levels of automation show risk-related idiosyncrasies. A lack of empirical data aggravates the transparent evaluation of risk arising from CAVs with higher levels of automation capability. Nevertheless, it is likely that the risks associated with CAV will profoundly reshape the risk profile of the global motor insurance industry. This paper conducts a deep qualitative analysis of the impact of progressive vehicle automation and interconnectedness on the risks covered under motor third-party and comprehensive insurance policies. This analysis is enhanced by an assessment of potential emerging risks such as the risk of cyber-attacks. We find that, in particular, primary insurers focusing on private retail motor insurance face significant strategic risks to their business model. The results of this analysis are not only relevant for insurance but also from a regulatory perspective as we find a symbiotic relationship between an insurance-related assessment and a comprehensive evaluation of CAV’s inherent societal costs.

Test procedure for evaluating the human–machine interface of vehicles with automated driving systems

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this.Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA’s automated vehicle policy are met.Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics.Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.

Incorporating Attitudinal Factors to Examine Adoption of and Willingness to Pay for Autonomous Vehicles

Results from a recent consumer survey were thoroughly analyzed in relation to willingness to adopt and willingness to pay (WTP) for different autonomous vehicle (AV) features. Four different levels of automation were considered including basic vehicles, adding advanced features, partial automation, and full automation. A structural equations model with latent variables was employed, which simultaneously regressed adoption and WTP levels against a variety of available variables including socioeconomic and demographic attributes, private car usage habits, and attitudinal preferences/personal opinions. To address the endogeneity in personal attitudes, these variables were added to the model as latent factors. Accordingly, the analysis revealed four major latent attitudinal factors, respectively labeled as “joy of driving,”“mode choice reasoning,”“trust,” and “technology savviness.” Model results indicated that those who enjoy driving were the hardest to persuade towards AV adoption or to pay for automated features. On the other hand, technology savvy people showed higher tendency towards AV adoption. When it comes to factors affecting mode choice including travel time, travel cost, and functionality, people are willing to pay more for automated features when they believe that these features and services will provide them better utility, in relation to time and cost savings, convenience, stress reduction, and quality of life, and so forth. Interestingly, individuals with trust concerns showed higher WTP values, which may indicate that the market believes autonomous vehicles will bring more privacy and protection, at least compared with existing shared mobility or public transit options.

Considering the wellbeing implications for an ageing population of a transition to automated vehicles

As populations around the world age, the challenge of ensuring that transport systems can contribute to the wellbeing of older adults becomes more pertinent. Maintaining wellbeing is particularly challenging for older adults in car-dependent societies who have to stop driving at some point in life. It is widely suggested that automated transport will provide a technological solution to the challenges of reduced accessibility and mobility faced by older adults who can no longer drive.In this position paper, we explore the potential wellbeing implications for an ageing population of a transition to automated vehicles and consequent social and spatial shifts in the context of Aotearoa New Zealand, which is a highly car dependent nation. We discuss the processes through which different levels of automation, and different models of vehicle access, could result in different wellbeing outcomes through changes to travel behaviours and urban form.We conclude by discussing the implications of our findings for transport policy and managerial practice, both in Aotearoa New Zealand and cities globally. Ultimately, we argue that the efficacy of emerging transport technologies for ageing populations depends on the extent to which new technologies are accessible and affordable for older people and how far they can support development of denser, less car reliant urban environments. Achieving this is a challenge for transport management.

A Continuous and Quantitative Metric for the Levels of Automation

The standard SAE J3016 provides a definition for levels of driving automation; however, shared control algorithms that have successfully been applied to vehicle automation, do not reasonably fit into the levels proposed by the standard. In this paper, the rich literature about levels of automation taxonomies is reviewed regarding the applicability to all automation concepts. Most definitions provide qualitative rankings of different levels of automation for specific applications. However, from an engineering perspective, a quantitative and generic approach would generate great benefit for the design, analysis and evaluation of human-machine systems. Thus, criteria for a desirable metric of levels of automation are discussed and a continuous and quantitative measure based on the options available to the human is proposed. The application to an advanced driver assistant system example is demonstrated and future ways to leverage the potential of the metric within and beyond automated driving are examined.

Towards an Extensible Architecture for Ideation

Ideation is an important activity of Design Thinking, a process that may benefit from different levels of automation in its activities. Preceded by Immersion and Analysis activities, Ideation can be enhanced by computational approaches like debate synthesis and mediation, sentiment analysis, and so on. In this paper an architecture for an extensible platform for ideation is addressed. Initially, it comprises a set of components to cope with those functionalities. The extensibility of this platform is in the sense that it shall allow the inclusion of new components like creation of domain ontologies for integration of different studies in the same domain. The general purpose of this platform is to support the creation of ideas by (i) constructing consensus among specialists and (ii) managing dissents in order to keep in track of marginal ideas that can become dominant ones as the discussion advances. It can be applied to many fields, like innovation, Studies of Future, definition of complex diagnoses, etc. Actually, this proposal came up from an experience with a study of future in which some experts had tried to envision trends for some years ahead in order to propose strategic actions for reaching a desired status for Brazil as a successful, fair, and inclusive country. The proposal includes an open and interactive computational environment to enable (i) structuring debates in threads of discussion; (ii) the gathering of ideas about topics of interest; (iii) the debate on the ideas put forward in order to identify the most relevant ones; (iv) synthesis of a debate (anytime summarization); (v) identification of the prevailing sentiments in a debate; and (vi) identification of variables relevant for the sake of the debate target.

Human adaptation to latency in teleoperated multi-robot human-agent search and rescue teams

Abstract Teleoperation of unmanned vehicles in high stress environments has been a subject of research in many domains, which focus primarily on system and operator performance. Unmanned ground vehicles for rescue, also known as search and rescue robots, serve as extensions of responders in a disaster, providing real-time video and other relevant information about the situation. However, physically separating responder and robot introduces latency between the human input provided to the unmanned vehicle to execute an operation and the subsequent response provided by the system. This latency (lag or time delay) is determined by the distance and the bandwidth of the connection between the operator and the unmanned vehicle. Automating these systems may mitigate the effect of latency to an extent; however, this has its own consequences, such as leaving the responder out of the loop, which subsequently leads to detrimental effects on situational awareness. This research investigates the relationship between latency and the performance of the human operator of a teleoperated robot at different levels of system complexity and the effect of different levels of automation on this relationship. Eighty participants operated one or two unmanned teleoperated robots to complete two search and rescue tasks. The study utilized a 2 × 2 × 2 mixed-subjects experimental design with the automation level and latency level being the between-subjects factors and the system complexity (controlling one or two robots) being the within-subjects factor. The dependent variables were operator performance, perceived workload, and the subjective rating of trust with automation. A latency of 500 ms showed a significant decrease in performance in time to complete the task and a significant increase in the perceived physical workload. Both the automation level and latency level moderated the system complexity effect on the subjective rating of trust in the robotic system. The level of trust decreased over time in the one-robot condition as opposed to no change in the two-robot condition. The error rate decreased over time at different rates based on the number of robots or the latency level. Based on the results of the study, several design implications are suggested for improving performance including adding features to the automation that will allow the operator to use common strategies and providing necessary information using multiple sensory channels. Future research directions are also proposed.

Not All Technological Change is Equal: Disentangling Labor Demand Effects of Automation and Parts Consolidation

We measure the labor-demand effects of two simultaneous forms of technological change—automation of production processes and consolidation of parts. We collect detailed shop-floor data from four semiconductor firms with different levels of automation and consolidation. Using the O*NET survey instrument, we collect novel task data for operator laborers that contains process-step level skill requirements, including operations and control, near vision, and dexterity requirements. We then use an engineering process model to separate the effects of the distinct technological changes on these process tasks and operator skill requirements. Within an occupation, we show that aggregate measures of technological change can mask the opposing skill biases of multiple simultaneous technological changes. In our empirical context, automation polarizes skill demand as routine, codifiable tasks requiring low and medium skills are executed by machines instead of humans, while the remaining and newly created human tasks tend to require low and high skills. Consolidation converges skill demand as formerly divisible low and high skill tasks are transformed into a single indivisible task with medium skill requirements and higher cost of failure. We conclude by developing a new theory for how the separability of tasks mediates the effect of technology change on skill demand by changing the divisibility of labor.

Integrating Different Levels of Automation: Lessons From Winning the Amazon Robotics Challenge 2016

This paper describes Team Delft's robot winning the Amazon Robotics Challenge 2016. The competition involves automating pick and place operations in semistructured environments, specifically the shelves in an Amazon warehouse. Team Delft's entry demonstrated that the current robot technology can already address most of the challenges in product handling: object recognition, grasping, motion, or task planning; under broad yet bounded conditions. The system combines an industrial robot arm, 3-D cameras and a custom gripper. The robot's software is based on the robot operating system to implement solutions based on deep learning and other state-of-the-art artificial intelligence techniques, and to integrate them with off-the-shelf components. From the experience developing the robotic system, it was concluded that: 1) the specific task conditions should guide the selection of the solution for each capability required; 2) understanding the characteristics of the individual solutions and the assumptions they embed is critical to integrate a performing system from them; and 3) this characterization can be based on “levels of robot automation.” This paper proposes automation levels based on the usage of information at design or runtime to drive the robot's behavior, and uses them to discuss Team Delft's design solution and the lessons learned from this robot development experience.

Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection

Bomb attacks on civil aviation make detecting improvised explosive devices and explosive material in passenger baggage a major concern. In the last few years, explosive detection systems for cabin baggage screening (EDSCB) have become available. Although used by a number of airports, most countries have not yet implemented these systems on a wide scale. We investigated the benefits of EDSCB with two different levels of automation currently being discussed by regulators and airport operators: automation as a diagnostic aid with an on-screen alarm resolution by the airport security officer (screener) or EDSCB with an automated decision by the machine. The two experiments reported here tested and compared both scenarios and a condition without automation as baseline. Participants were screeners at two international airports who differed in both years of work experience and familiarity with automation aids. Results showed that experienced screeners were good at detecting improvised explosive devices even without EDSCB. EDSCB increased only their detection of bare explosives. In contrast, screeners with less experience (tenure < 1 year) benefitted substantially from EDSCB in detecting both improvised explosive devices and bare explosives. A comparison of all three conditions showed that automated decision provided better human–machine detection performance than on-screen alarm resolution and no automation. This came at the cost of slightly higher false alarm rates on the human–machine system level, which would still be acceptable from an operational point of view. Results indicate that a wide-scale implementation of EDSCB would increase the detection of explosives in passenger bags and automated decision instead of automation as diagnostic aid with on screen alarm resolution should be considered.

Biomechanical exposure of industrial workers – Influence of automation process

Automated processes in industry have been implemented in order to get higher production rates, but our knowledge about their effects on physical exposure of the workers is still limited. Muscular load, postures, and movements of the head, neck/shoulders, upper arms, and wrists were recorded in 19 operators from two different car clutch disc production lines (manual and semi-automated). Higher angular velocities of the head, upper back, and upper arms were found on the manual production line (on average 20% higher than the semi-automated). Upper trapezius, and forearm extensor rest (% time), as well as hand kept still (% time) were also higher on the manual production line. No difference was found regarding posture, muscular activities, and repetitiveness. The manual line had more rest, but more vigorous movements considering angular velocities. The semi-automated line, therefore, implied a higher production rate with lower angular velocities but fewer opportunities for rest than the manual line. While different physical exposures were found when comparing these two production lines with different levels of automation, the health effects derived from industrial automation ought to be investigated with a larger sample size. Relevance to industryThe reduction of rest opportunities observed on a semi-automated production line requires the attention of ergonomists when planning or redesigning tasks in such production lines. Lack of rest is considered a risk factor for the development of musculoskeletal disorders.