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Towards a unified taxonomy for algorithmic transparency
Insights from uncrewed air traffic management
With the rapid advancement of drone technology, their applications have become increasingly widespread. However, the integration of drones into the airspace also poses risks to crewed aircraft, particularly around airports. To address this issue, a highly automated Uncrewed Air Traffic Management (UTM) system is being developed. Since fully safe and reliable automation does not exist yet, UTM still requires human supervision to enhance the overall system safety and reliability. Some form of “seeing-into” transparency may be necessary to help operators better understand the limitations and behavior of the automated UTM system. As UTM is a novel concept, research on transparent UTM is limited. Many efforts have been made in other fields, but there still remains a lack of consensus on what transparency entails, particularly for algorithmic systems. Therefore, this article first presents a unified taxonomy for algorithmic transparency, with operational, domain and engineering transparency introduced as its core concepts. From the taxonomy, twenty UTM transparency elements and their corresponding visual prototypes were then designed, which also showcases how the taxonomy can be applied in practice. A survey-based user study was conducted to collect the opinions of air traffic controllers and drone experts regarding the designed elements and prototypes. Results indicate that transparency is deemed imperative for UTM, especially in scenarios featuring automation failure. It also reveals that operational transparency is generally preferable over engineering transparency in nominal operations. Participants were asked to group the designed elements, and their results closely aligned with the structure of the proposed taxonomy.
...
With the rapid advancement of drone technology, their applications have become increasingly widespread. However, the integration of drones into the airspace also poses risks to crewed aircraft, particularly around airports. To address this issue, a highly automated Uncrewed Air Traffic Management (UTM) system is being developed. Since fully safe and reliable automation does not exist yet, UTM still requires human supervision to enhance the overall system safety and reliability. Some form of “seeing-into” transparency may be necessary to help operators better understand the limitations and behavior of the automated UTM system. As UTM is a novel concept, research on transparent UTM is limited. Many efforts have been made in other fields, but there still remains a lack of consensus on what transparency entails, particularly for algorithmic systems. Therefore, this article first presents a unified taxonomy for algorithmic transparency, with operational, domain and engineering transparency introduced as its core concepts. From the taxonomy, twenty UTM transparency elements and their corresponding visual prototypes were then designed, which also showcases how the taxonomy can be applied in practice. A survey-based user study was conducted to collect the opinions of air traffic controllers and drone experts regarding the designed elements and prototypes. Results indicate that transparency is deemed imperative for UTM, especially in scenarios featuring automation failure. It also reveals that operational transparency is generally preferable over engineering transparency in nominal operations. Participants were asked to group the designed elements, and their results closely aligned with the structure of the proposed taxonomy.
With the rapid advancement of technology, drones are being actively deployed across various domains. To manage their growing presence in the airspace, Uncrewed Air Traffic Management (UTM) has been proposed and is currently under development. Given the expected high volume of drone traffic, UTM will rely heavily on high levels of automation, as it is impractical to control each drone manually in the manner of traditional Air Traffic Control (ATC). However, this reliance on automation presents potential risks, particularly in airspace around airports where crewed aircraft are taking off and landing. Since completely reliable automation has not yet been achieved, anomalies or failures in UTM systems could increase the risk of collisions between drones and crewed aircraft. Therefore, UTM still warrants human supervision to ensure the safety of drone operations.
As automation becomes more advanced and complex, it also becomes increasingly difficult for humans to supervise, thereby hindering their trust and acceptance. Previous research suggests that some form of “seeing-into” transparency may be required to address this issue and support effective human supervision of automated systems. In this dissertation, “seeing-into” transparency is categorised into operational transparency and engineering transparency. Operational transparency offers (real-time) insights into the automation’s states, actions, goals, and environmental impact, helping operational users maintain situation awareness and respond effectively to changing conditions. Engineering transparency, in contrast, discloses the inner workings of automation, enabling users to develop a deeper understanding of automation behaviour. This research adopts a bottom-up approach, beginning with engineering transparency and progressing towards operational transparency.
This dissertation focuses on achieving transparent path planning in UTM routing. To this end, a visual approach was first proposed to reveal the internal processes of path-planning algorithms, with a focus on graph- and sampling-based ones, as shown in Chapter 2. To demonstrate the effectiveness of the approach, a novel web-based pathfinding visualiser was developed that incorporates various classic and advanced path-planning algorithms, such as A*, Theta*, Anya, Polyanya, Rapidly-exploring Random Tree (RRT), RRT*, Informed RRT* and Batch Informed Tree (BIT*). To evaluate the impact of the proposed approach on algorithm runtime, extensive benchmark tests were performed on public datasets. Results show that extracting all search trees during the search process may significantly slow down the original algorithms. For large-scale, real-time operations, it is recommended to record only necessary data during the search and perform search tree extraction afterwards for visualisation.
To further investigate the effectiveness of algorithmic transparency, a user study was conducted to evaluate its impact on human understanding, as presented in Chapter 3. Considering that directly presenting the search process may overwhelm users, particularly in operational contexts, the path-planning transparency was structured into six distinct levels. Results indicate that as the transparency level increases, so does human understanding. However, the relationship between transparency and understanding is not a linear one. When the algorithm behaves contrary to human expectations and increased transparency fails to provide a clear explanation, users may become even more confused than without the additional transparency. For non-expert users unfamiliar with the algorithm, full transparency is often critical for meaningful understanding. The user study suggests that sampling-based algorithms may be easier to comprehend than graph-based algorithms. While the randomness inherent in sampling-based algorithms makes their behaviour difficult to predict, their overall rationale and underlying principles are meaningful and intuitive to humans.
As the ultimate goal of this dissertation is to achieve transparent path planning for UTM, the focus was then shifted from path-planning algorithms to UTM routing, broadening the concept of algorithmic transparency from purely engineering concerns to encompass operational dimensions, as shown in Chapter 4. A unified transparency taxonomy was developed, integrating diverse aspects of algorithmic transparency. Based on the proposed taxonomy, twenty transparency elements and their corresponding visual prototypes were devised for UTM routing. A survey study was then conducted to investigate the needs and preferences of Air Traffic Controllers (ATCos) and drone experts regarding these elements and prototypes. Results show that operational transparency is deemed more useful than engineering transparency in nominal UTM scenarios, whereas engineering transparency becomes more valuable when UTM routing fails. In the survey, operators were also asked to group the transparency elements, and their groupings aligned with the proposed transparency taxonomy.
The survey study captures only the initial opinions of operators, shaped by their prior knowledge and experience. To gain more insights, a human-in-the-loop experiment was performed to further examine the actual usage of various transparency elements in dynamic scenarios, where time pressure is a key concern, as shown in Chapter 5. Results show that information regarding the Closest Point of Approach (CPA) between drones and crewed aircraft is the most useful element for supporting tactical UTM supervision. When UTM routing fails, operators typically seek more information, such as constraint changes and details about the algorithm’s inner workings, to understand the failure and to gather clues that inform their intervention strategies. Similar to the user study presented in Chapter 3, the experiment in Chapter 5 also suggests that in UTM contexts, sampling-based algorithms might be more suitable for supervision than graph-based algorithms. This is likely because the search tree visualisation of sampling-based algorithms could more clearly convey the algorithms’ exploration efforts, offering useful cues for human intervention, such as indicating regions that are likely to be conflict-free.
In conclusion, this research achieves algorithmic transparency in path planning and demonstrates its application within UTM contexts. It contributes further empirical evidence to the growing body of research underscoring the importance and benefits of algorithmic transparency. The findings suggest that algorithmic transparency can enhance human understanding, but its utility in operational settings may be limited by situations, time pressure, and workload. As operators develop trust or expertise, their need for transparency may diminish. Overall, transparency is essential to facilitating trustworthy automation, especially when it is not yet fully reliable. ...
As automation becomes more advanced and complex, it also becomes increasingly difficult for humans to supervise, thereby hindering their trust and acceptance. Previous research suggests that some form of “seeing-into” transparency may be required to address this issue and support effective human supervision of automated systems. In this dissertation, “seeing-into” transparency is categorised into operational transparency and engineering transparency. Operational transparency offers (real-time) insights into the automation’s states, actions, goals, and environmental impact, helping operational users maintain situation awareness and respond effectively to changing conditions. Engineering transparency, in contrast, discloses the inner workings of automation, enabling users to develop a deeper understanding of automation behaviour. This research adopts a bottom-up approach, beginning with engineering transparency and progressing towards operational transparency.
This dissertation focuses on achieving transparent path planning in UTM routing. To this end, a visual approach was first proposed to reveal the internal processes of path-planning algorithms, with a focus on graph- and sampling-based ones, as shown in Chapter 2. To demonstrate the effectiveness of the approach, a novel web-based pathfinding visualiser was developed that incorporates various classic and advanced path-planning algorithms, such as A*, Theta*, Anya, Polyanya, Rapidly-exploring Random Tree (RRT), RRT*, Informed RRT* and Batch Informed Tree (BIT*). To evaluate the impact of the proposed approach on algorithm runtime, extensive benchmark tests were performed on public datasets. Results show that extracting all search trees during the search process may significantly slow down the original algorithms. For large-scale, real-time operations, it is recommended to record only necessary data during the search and perform search tree extraction afterwards for visualisation.
To further investigate the effectiveness of algorithmic transparency, a user study was conducted to evaluate its impact on human understanding, as presented in Chapter 3. Considering that directly presenting the search process may overwhelm users, particularly in operational contexts, the path-planning transparency was structured into six distinct levels. Results indicate that as the transparency level increases, so does human understanding. However, the relationship between transparency and understanding is not a linear one. When the algorithm behaves contrary to human expectations and increased transparency fails to provide a clear explanation, users may become even more confused than without the additional transparency. For non-expert users unfamiliar with the algorithm, full transparency is often critical for meaningful understanding. The user study suggests that sampling-based algorithms may be easier to comprehend than graph-based algorithms. While the randomness inherent in sampling-based algorithms makes their behaviour difficult to predict, their overall rationale and underlying principles are meaningful and intuitive to humans.
As the ultimate goal of this dissertation is to achieve transparent path planning for UTM, the focus was then shifted from path-planning algorithms to UTM routing, broadening the concept of algorithmic transparency from purely engineering concerns to encompass operational dimensions, as shown in Chapter 4. A unified transparency taxonomy was developed, integrating diverse aspects of algorithmic transparency. Based on the proposed taxonomy, twenty transparency elements and their corresponding visual prototypes were devised for UTM routing. A survey study was then conducted to investigate the needs and preferences of Air Traffic Controllers (ATCos) and drone experts regarding these elements and prototypes. Results show that operational transparency is deemed more useful than engineering transparency in nominal UTM scenarios, whereas engineering transparency becomes more valuable when UTM routing fails. In the survey, operators were also asked to group the transparency elements, and their groupings aligned with the proposed transparency taxonomy.
The survey study captures only the initial opinions of operators, shaped by their prior knowledge and experience. To gain more insights, a human-in-the-loop experiment was performed to further examine the actual usage of various transparency elements in dynamic scenarios, where time pressure is a key concern, as shown in Chapter 5. Results show that information regarding the Closest Point of Approach (CPA) between drones and crewed aircraft is the most useful element for supporting tactical UTM supervision. When UTM routing fails, operators typically seek more information, such as constraint changes and details about the algorithm’s inner workings, to understand the failure and to gather clues that inform their intervention strategies. Similar to the user study presented in Chapter 3, the experiment in Chapter 5 also suggests that in UTM contexts, sampling-based algorithms might be more suitable for supervision than graph-based algorithms. This is likely because the search tree visualisation of sampling-based algorithms could more clearly convey the algorithms’ exploration efforts, offering useful cues for human intervention, such as indicating regions that are likely to be conflict-free.
In conclusion, this research achieves algorithmic transparency in path planning and demonstrates its application within UTM contexts. It contributes further empirical evidence to the growing body of research underscoring the importance and benefits of algorithmic transparency. The findings suggest that algorithmic transparency can enhance human understanding, but its utility in operational settings may be limited by situations, time pressure, and workload. As operators develop trust or expertise, their need for transparency may diminish. Overall, transparency is essential to facilitating trustworthy automation, especially when it is not yet fully reliable. ...
With the rapid advancement of technology, drones are being actively deployed across various domains. To manage their growing presence in the airspace, Uncrewed Air Traffic Management (UTM) has been proposed and is currently under development. Given the expected high volume of drone traffic, UTM will rely heavily on high levels of automation, as it is impractical to control each drone manually in the manner of traditional Air Traffic Control (ATC). However, this reliance on automation presents potential risks, particularly in airspace around airports where crewed aircraft are taking off and landing. Since completely reliable automation has not yet been achieved, anomalies or failures in UTM systems could increase the risk of collisions between drones and crewed aircraft. Therefore, UTM still warrants human supervision to ensure the safety of drone operations.
As automation becomes more advanced and complex, it also becomes increasingly difficult for humans to supervise, thereby hindering their trust and acceptance. Previous research suggests that some form of “seeing-into” transparency may be required to address this issue and support effective human supervision of automated systems. In this dissertation, “seeing-into” transparency is categorised into operational transparency and engineering transparency. Operational transparency offers (real-time) insights into the automation’s states, actions, goals, and environmental impact, helping operational users maintain situation awareness and respond effectively to changing conditions. Engineering transparency, in contrast, discloses the inner workings of automation, enabling users to develop a deeper understanding of automation behaviour. This research adopts a bottom-up approach, beginning with engineering transparency and progressing towards operational transparency.
This dissertation focuses on achieving transparent path planning in UTM routing. To this end, a visual approach was first proposed to reveal the internal processes of path-planning algorithms, with a focus on graph- and sampling-based ones, as shown in Chapter 2. To demonstrate the effectiveness of the approach, a novel web-based pathfinding visualiser was developed that incorporates various classic and advanced path-planning algorithms, such as A*, Theta*, Anya, Polyanya, Rapidly-exploring Random Tree (RRT), RRT*, Informed RRT* and Batch Informed Tree (BIT*). To evaluate the impact of the proposed approach on algorithm runtime, extensive benchmark tests were performed on public datasets. Results show that extracting all search trees during the search process may significantly slow down the original algorithms. For large-scale, real-time operations, it is recommended to record only necessary data during the search and perform search tree extraction afterwards for visualisation.
To further investigate the effectiveness of algorithmic transparency, a user study was conducted to evaluate its impact on human understanding, as presented in Chapter 3. Considering that directly presenting the search process may overwhelm users, particularly in operational contexts, the path-planning transparency was structured into six distinct levels. Results indicate that as the transparency level increases, so does human understanding. However, the relationship between transparency and understanding is not a linear one. When the algorithm behaves contrary to human expectations and increased transparency fails to provide a clear explanation, users may become even more confused than without the additional transparency. For non-expert users unfamiliar with the algorithm, full transparency is often critical for meaningful understanding. The user study suggests that sampling-based algorithms may be easier to comprehend than graph-based algorithms. While the randomness inherent in sampling-based algorithms makes their behaviour difficult to predict, their overall rationale and underlying principles are meaningful and intuitive to humans.
As the ultimate goal of this dissertation is to achieve transparent path planning for UTM, the focus was then shifted from path-planning algorithms to UTM routing, broadening the concept of algorithmic transparency from purely engineering concerns to encompass operational dimensions, as shown in Chapter 4. A unified transparency taxonomy was developed, integrating diverse aspects of algorithmic transparency. Based on the proposed taxonomy, twenty transparency elements and their corresponding visual prototypes were devised for UTM routing. A survey study was then conducted to investigate the needs and preferences of Air Traffic Controllers (ATCos) and drone experts regarding these elements and prototypes. Results show that operational transparency is deemed more useful than engineering transparency in nominal UTM scenarios, whereas engineering transparency becomes more valuable when UTM routing fails. In the survey, operators were also asked to group the transparency elements, and their groupings aligned with the proposed transparency taxonomy.
The survey study captures only the initial opinions of operators, shaped by their prior knowledge and experience. To gain more insights, a human-in-the-loop experiment was performed to further examine the actual usage of various transparency elements in dynamic scenarios, where time pressure is a key concern, as shown in Chapter 5. Results show that information regarding the Closest Point of Approach (CPA) between drones and crewed aircraft is the most useful element for supporting tactical UTM supervision. When UTM routing fails, operators typically seek more information, such as constraint changes and details about the algorithm’s inner workings, to understand the failure and to gather clues that inform their intervention strategies. Similar to the user study presented in Chapter 3, the experiment in Chapter 5 also suggests that in UTM contexts, sampling-based algorithms might be more suitable for supervision than graph-based algorithms. This is likely because the search tree visualisation of sampling-based algorithms could more clearly convey the algorithms’ exploration efforts, offering useful cues for human intervention, such as indicating regions that are likely to be conflict-free.
In conclusion, this research achieves algorithmic transparency in path planning and demonstrates its application within UTM contexts. It contributes further empirical evidence to the growing body of research underscoring the importance and benefits of algorithmic transparency. The findings suggest that algorithmic transparency can enhance human understanding, but its utility in operational settings may be limited by situations, time pressure, and workload. As operators develop trust or expertise, their need for transparency may diminish. Overall, transparency is essential to facilitating trustworthy automation, especially when it is not yet fully reliable.
As automation becomes more advanced and complex, it also becomes increasingly difficult for humans to supervise, thereby hindering their trust and acceptance. Previous research suggests that some form of “seeing-into” transparency may be required to address this issue and support effective human supervision of automated systems. In this dissertation, “seeing-into” transparency is categorised into operational transparency and engineering transparency. Operational transparency offers (real-time) insights into the automation’s states, actions, goals, and environmental impact, helping operational users maintain situation awareness and respond effectively to changing conditions. Engineering transparency, in contrast, discloses the inner workings of automation, enabling users to develop a deeper understanding of automation behaviour. This research adopts a bottom-up approach, beginning with engineering transparency and progressing towards operational transparency.
This dissertation focuses on achieving transparent path planning in UTM routing. To this end, a visual approach was first proposed to reveal the internal processes of path-planning algorithms, with a focus on graph- and sampling-based ones, as shown in Chapter 2. To demonstrate the effectiveness of the approach, a novel web-based pathfinding visualiser was developed that incorporates various classic and advanced path-planning algorithms, such as A*, Theta*, Anya, Polyanya, Rapidly-exploring Random Tree (RRT), RRT*, Informed RRT* and Batch Informed Tree (BIT*). To evaluate the impact of the proposed approach on algorithm runtime, extensive benchmark tests were performed on public datasets. Results show that extracting all search trees during the search process may significantly slow down the original algorithms. For large-scale, real-time operations, it is recommended to record only necessary data during the search and perform search tree extraction afterwards for visualisation.
To further investigate the effectiveness of algorithmic transparency, a user study was conducted to evaluate its impact on human understanding, as presented in Chapter 3. Considering that directly presenting the search process may overwhelm users, particularly in operational contexts, the path-planning transparency was structured into six distinct levels. Results indicate that as the transparency level increases, so does human understanding. However, the relationship between transparency and understanding is not a linear one. When the algorithm behaves contrary to human expectations and increased transparency fails to provide a clear explanation, users may become even more confused than without the additional transparency. For non-expert users unfamiliar with the algorithm, full transparency is often critical for meaningful understanding. The user study suggests that sampling-based algorithms may be easier to comprehend than graph-based algorithms. While the randomness inherent in sampling-based algorithms makes their behaviour difficult to predict, their overall rationale and underlying principles are meaningful and intuitive to humans.
As the ultimate goal of this dissertation is to achieve transparent path planning for UTM, the focus was then shifted from path-planning algorithms to UTM routing, broadening the concept of algorithmic transparency from purely engineering concerns to encompass operational dimensions, as shown in Chapter 4. A unified transparency taxonomy was developed, integrating diverse aspects of algorithmic transparency. Based on the proposed taxonomy, twenty transparency elements and their corresponding visual prototypes were devised for UTM routing. A survey study was then conducted to investigate the needs and preferences of Air Traffic Controllers (ATCos) and drone experts regarding these elements and prototypes. Results show that operational transparency is deemed more useful than engineering transparency in nominal UTM scenarios, whereas engineering transparency becomes more valuable when UTM routing fails. In the survey, operators were also asked to group the transparency elements, and their groupings aligned with the proposed transparency taxonomy.
The survey study captures only the initial opinions of operators, shaped by their prior knowledge and experience. To gain more insights, a human-in-the-loop experiment was performed to further examine the actual usage of various transparency elements in dynamic scenarios, where time pressure is a key concern, as shown in Chapter 5. Results show that information regarding the Closest Point of Approach (CPA) between drones and crewed aircraft is the most useful element for supporting tactical UTM supervision. When UTM routing fails, operators typically seek more information, such as constraint changes and details about the algorithm’s inner workings, to understand the failure and to gather clues that inform their intervention strategies. Similar to the user study presented in Chapter 3, the experiment in Chapter 5 also suggests that in UTM contexts, sampling-based algorithms might be more suitable for supervision than graph-based algorithms. This is likely because the search tree visualisation of sampling-based algorithms could more clearly convey the algorithms’ exploration efforts, offering useful cues for human intervention, such as indicating regions that are likely to be conflict-free.
In conclusion, this research achieves algorithmic transparency in path planning and demonstrates its application within UTM contexts. It contributes further empirical evidence to the growing body of research underscoring the importance and benefits of algorithmic transparency. The findings suggest that algorithmic transparency can enhance human understanding, but its utility in operational settings may be limited by situations, time pressure, and workload. As operators develop trust or expertise, their need for transparency may diminish. Overall, transparency is essential to facilitating trustworthy automation, especially when it is not yet fully reliable.
Algorithmic transparency in path planning
A visual approach to enhancing human understanding
Computer algorithms facilitate increased automation in various human-centered work areas to improve operational safety and efficiency. Algorithmic transparency is considered essential for human operators, policy makers and system developers, as it allows them to understand the capabilities and limitations of an algorithm. In this research, we focus on path-planning algorithms and propose a purely visual approach to achieve their transparency. This approach extracts and portrays information directly from the algorithms, aiming to visually reveal their inner workings. Benchmark tests indicate that extracting information from path-planning algorithms may significantly slow them down. For time-constrained operations, it is recommended to store only the necessary data during the pathfinding process and perform information extraction afterwards. Based on theories from cognitive engineering, six transparency levels were designed to chunk meaningful information pertaining path-planning algorithms. A user study among non-experts (N=40) was then conducted to evaluate the impact of visual algorithmic transparency on human understanding. The results suggest that increased transparency levels allow non-experts to more correctly and confidently understand the details of a path-planning algorithm. However, it is also found that certain transparency levels can lead to confusion, especially when the algorithm behaves in a way contrary to human expectations. This study further reveals that, given the same level of transparency, sampling-based algorithms may be easier to comprehend than graph-based algorithms. This research can serve as a reference for how to achieve transparency in path-planning-related applications and how to hierarchically portray and organize transparency information.
...
Computer algorithms facilitate increased automation in various human-centered work areas to improve operational safety and efficiency. Algorithmic transparency is considered essential for human operators, policy makers and system developers, as it allows them to understand the capabilities and limitations of an algorithm. In this research, we focus on path-planning algorithms and propose a purely visual approach to achieve their transparency. This approach extracts and portrays information directly from the algorithms, aiming to visually reveal their inner workings. Benchmark tests indicate that extracting information from path-planning algorithms may significantly slow them down. For time-constrained operations, it is recommended to store only the necessary data during the pathfinding process and perform information extraction afterwards. Based on theories from cognitive engineering, six transparency levels were designed to chunk meaningful information pertaining path-planning algorithms. A user study among non-experts (N=40) was then conducted to evaluate the impact of visual algorithmic transparency on human understanding. The results suggest that increased transparency levels allow non-experts to more correctly and confidently understand the details of a path-planning algorithm. However, it is also found that certain transparency levels can lead to confusion, especially when the algorithm behaves in a way contrary to human expectations. This study further reveals that, given the same level of transparency, sampling-based algorithms may be easier to comprehend than graph-based algorithms. This research can serve as a reference for how to achieve transparency in path-planning-related applications and how to hierarchically portray and organize transparency information.
Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.
...
Any-angle path planning is an extension of traditional path-planning algorithms that aims to generate smoother and shorter paths in graphs by allowing any-angle moves between vertices, rather than being restricted by edges. Many any-angle path-planning algorithms have been proposed, such as Theta*, Block A* and Anya, but most of them are designed only for static environments, which is not applicable when dynamic obstacles are present. Time-Optimal Any-Angle Safe-Interval Path Planning (TO-AA-SIPP) was developed to fill this gap, which can find an optimal collision-free any-angle path that minimizes the traversal time. However, as indicated by its authors, TO-AA-SIPP may not be efficient enough to be used in multi-agent pathfinding (MAPF). Therefore, this paper presents a new algorithm Zeta*-SIPP to improve TO-AA-SIPP by means of 1) reducing useless search nodes that have no contribution to finding optimal solutions, and 2) introducing Field of View (FoV) instead of Line of Sight (LoS) to speed up visibility checks with static obstacles. Benchmark experiments showed that Zeta*-SIPP reduced the computation time of TO-AA-SIPP by around 70%-90% on average.
As concepts for incorporating uncrewed aerial vehicles (UAS) into controlled airspace are being developed, the need for automated UAS traffic management (UTM) systems to guide UAS and maintain safety is becoming more apparent. A major point of concern for the implementation of UTM is how such systems could coexist alongside the human-centric air traffic management system that is already in place. The European Union’s U-space concept proposes the use of dynamic segregation of airspace reserved for UAS within the control zone. We conducted a simulation experiment with ten air traffic control officer (ATCO) volunteers to gather insights into the feasibility of tower controllers performing the dynamic segregation task. An interface prototype that supports dynamic geofencing and low-level UAS control was developed for this purpose. We found that our proposed interface design helped ATCOs detect potential conflicts between UAS and crewed aircraft. However, they were not always able to adequately resolve them, which resulted in several loss of separation events. It appears that the limitations of the dynamic segregation concept do not fit well with typical air traffic control strategies used by ATCOs. To substantiate our findings, we propose future research to investigate how to overcome the limitations of dynamic segregation to resolve tactical conflicts by revising ATCO control strategies, reevaluating their role in dynamic segregation, as well as considering the definition of flight rules and separation minima for UAS.
...
As concepts for incorporating uncrewed aerial vehicles (UAS) into controlled airspace are being developed, the need for automated UAS traffic management (UTM) systems to guide UAS and maintain safety is becoming more apparent. A major point of concern for the implementation of UTM is how such systems could coexist alongside the human-centric air traffic management system that is already in place. The European Union’s U-space concept proposes the use of dynamic segregation of airspace reserved for UAS within the control zone. We conducted a simulation experiment with ten air traffic control officer (ATCO) volunteers to gather insights into the feasibility of tower controllers performing the dynamic segregation task. An interface prototype that supports dynamic geofencing and low-level UAS control was developed for this purpose. We found that our proposed interface design helped ATCOs detect potential conflicts between UAS and crewed aircraft. However, they were not always able to adequately resolve them, which resulted in several loss of separation events. It appears that the limitations of the dynamic segregation concept do not fit well with typical air traffic control strategies used by ATCOs. To substantiate our findings, we propose future research to investigate how to overcome the limitations of dynamic segregation to resolve tactical conflicts by revising ATCO control strategies, reevaluating their role in dynamic segregation, as well as considering the definition of flight rules and separation minima for UAS.
To facilitate a smooth integration of drones into the current Air Traffic Management (ATM) system, Unmanned Air Traffic Management (UTM) systems, services and protocols are currently under development. Unlike current ATM, UTM will rely on high levels of automation. This is potentially problematic, because 100% safe and reliable automation under all circumstances cannot be guaranteed. UTM therefore warrants human supervision and interaction, especially at small airports near urban areas where drone traffic may cross the arrival and departure routes of manned air traffic. Supervision, however, requires some form of transparency for humans to understand the limitations and the behavior of an automated system (e.g., what is it currently doing, what it is planning to do next, and why?). Previous research underlined the importance of UTM transparency, but also indicated that it remains unclear what type of human operator will eventually supervise the UTM system. The background, training and expertise of a human operator may impact the transparency needs and what information needs to be communicated and when. In this paper, the results of a questionnaire-based user study are presented in which information needs were collected from twelve operational Air Traffic Controllers and twelve drone operators and engineers. Results indicate that transparency is deemed imperative for UTM and that information elements categorized as ‘operational transparency’ are typically preferable over ‘engineering transparency’ elements, regardless of operator group. Surprisingly, we found no significant difference in transparency needs between controllers and drone operators, suggesting that a ‘one-size-fits-all’ transparency solution for UTM would be possible.
...
To facilitate a smooth integration of drones into the current Air Traffic Management (ATM) system, Unmanned Air Traffic Management (UTM) systems, services and protocols are currently under development. Unlike current ATM, UTM will rely on high levels of automation. This is potentially problematic, because 100% safe and reliable automation under all circumstances cannot be guaranteed. UTM therefore warrants human supervision and interaction, especially at small airports near urban areas where drone traffic may cross the arrival and departure routes of manned air traffic. Supervision, however, requires some form of transparency for humans to understand the limitations and the behavior of an automated system (e.g., what is it currently doing, what it is planning to do next, and why?). Previous research underlined the importance of UTM transparency, but also indicated that it remains unclear what type of human operator will eventually supervise the UTM system. The background, training and expertise of a human operator may impact the transparency needs and what information needs to be communicated and when. In this paper, the results of a questionnaire-based user study are presented in which information needs were collected from twelve operational Air Traffic Controllers and twelve drone operators and engineers. Results indicate that transparency is deemed imperative for UTM and that information elements categorized as ‘operational transparency’ are typically preferable over ‘engineering transparency’ elements, regardless of operator group. Surprisingly, we found no significant difference in transparency needs between controllers and drone operators, suggesting that a ‘one-size-fits-all’ transparency solution for UTM would be possible.