Periodic Reporting for period 1 - ASTAIR (Auto-Steer Taxi at AIRport)
Periodo di rendicontazione: 2023-09-01 al 2024-08-31
While the most significant reductions in fuel consumption and noxious emissions in aviation occur during flight, these optimizations for aircraft trajectories are contingent on each flight adhering to its schedule. As a result, improving departure punctuality and smoothing trajectories become essential for realizing the full benefits of in-flight optimizations. Today, ground operations are managed by a human operator assisted with decision support tools. In addition, emergence of engine off taxiing techniques will raise the number of vehicles to guide because of additional towing tugs. Increasing the level of automation thanks to an Artificial Intelligence (AI) capable of planning conflict-free trajectories, for both departures and arrivals given their interdependent nature, and manage the routine movements autonomously, on behalf of the operator, could help increase the general predictability of airport turnaround operations.
ASTAIR goal is to design a seamless partnership between Human and AI to manage and perform engine-off and conventional taxiing operations on all the airport surfaces (including aircraft and towing vehicles steering from the gates to the runways) at major European airports.
To safely guide all vehicles on the taxiways, it is essential to manage not only the routes they follow but, most importantly, their speeds since it allows deconflicting trajectories. The SESAR AEON project has investigated the management of heterogeneous conventional and engine-off taxi traffic to reduce the taxiing environmental impact. In the resulting concept of operations (CONOPS), tug fleet managers and ground controllers work as a team relying on decision support tools to schedule autonomous resources allocation and optimize vehicle surface movements. Especially, the AI developed in the project AEON and reused in ASTAIR is capable of calculating conflict free routes for all vehicles through speed management.
Increasing the level of automation for both tug fleet managers and ground controllers in ASTAIR could have the potential to increase airport ground traffic capacity while mitigating the impact on human workload and the environment. For example, depending on the level of automation, AI will be able to initiate timely actions such as giving clearances to vehicles on the airport aprons and taxiways according to optimal routes.
Unfortunately, this routing with speed profiles cannot be implemented yet since aircraft taxiing on their jet engines are not finely operated. Nevertheless, solutions are being developed like Taxibot, autonomous follow-me cars, auto-taxi aircraft and these techniques will allow a better control over trajectories and speeds of mobiles. It becomes reasonable to envision future ground operations as being AI-driven with human supervision for routine tasks, while human-machine collaboration is employed to manage unexpected events or specific requirements. ASTAIR must consider the challenges of human-automation collaboration, not only in terms of interface design but also technical issues like computing time. These factors can create obstacles in mutual understanding and hinder the ability to share a consistent level of information between humans and machines due to processing delays. Challenges will arise in managing unforeseen situations and specific needs that AI may struggle to anticipate.
ASTAIR aims at defining automation capable of performing complex tasks involved in the management of surface engine-off and conventional taxiing while maintaining human engagement with the automation. Current airport taxi operation procedures have been tailored to optimize human performance while maintaining human workload to a level that does not compromise safety. With the taxi traffic capacity and the human cognitive availability increases offered by high-level automation and optimized execution support, the role of operators and airport operation procedures will significantly change.
ASTAIR objectives are to characterize levels of automation and identify pathways to full automation. For example, AEON's decisional routing support technologies can help shift towards an automated routing clearance system under operator supervision. We will explore all automation opportunities and target the 2B level per EASA’s AI Roadmap
ASTAIR goal is to design a seamless partnership between Human and AI to manage and perform engine-off and conventional taxiing operations on all the airport surfaces (including aircraft and towing vehicles steering from the gates to the runways) at major European airports.
To safely guide all vehicles on the taxiways, it is essential to manage not only the routes they follow but, most importantly, their speeds since it allows deconflicting trajectories. The SESAR AEON project has investigated the management of heterogeneous conventional and engine-off taxi traffic to reduce the taxiing environmental impact. In the resulting concept of operations (CONOPS), tug fleet managers and ground controllers work as a team relying on decision support tools to schedule autonomous resources allocation and optimize vehicle surface movements. Especially, the AI developed in the project AEON and reused in ASTAIR is capable of calculating conflict free routes for all vehicles through speed management.
Increasing the level of automation for both tug fleet managers and ground controllers in ASTAIR could have the potential to increase airport ground traffic capacity while mitigating the impact on human workload and the environment. For example, depending on the level of automation, AI will be able to initiate timely actions such as giving clearances to vehicles on the airport aprons and taxiways according to optimal routes.
Unfortunately, this routing with speed profiles cannot be implemented yet since aircraft taxiing on their jet engines are not finely operated. Nevertheless, solutions are being developed like Taxibot, autonomous follow-me cars, auto-taxi aircraft and these techniques will allow a better control over trajectories and speeds of mobiles. It becomes reasonable to envision future ground operations as being AI-driven with human supervision for routine tasks, while human-machine collaboration is employed to manage unexpected events or specific requirements. ASTAIR must consider the challenges of human-automation collaboration, not only in terms of interface design but also technical issues like computing time. These factors can create obstacles in mutual understanding and hinder the ability to share a consistent level of information between humans and machines due to processing delays. Challenges will arise in managing unforeseen situations and specific needs that AI may struggle to anticipate.
ASTAIR aims at defining automation capable of performing complex tasks involved in the management of surface engine-off and conventional taxiing while maintaining human engagement with the automation. Current airport taxi operation procedures have been tailored to optimize human performance while maintaining human workload to a level that does not compromise safety. With the taxi traffic capacity and the human cognitive availability increases offered by high-level automation and optimized execution support, the role of operators and airport operation procedures will significantly change.
ASTAIR objectives are to characterize levels of automation and identify pathways to full automation. For example, AEON's decisional routing support technologies can help shift towards an automated routing clearance system under operator supervision. We will explore all automation opportunities and target the 2B level per EASA’s AI Roadmap
During the first validation session, several interviews and workshops were held with airport ground operations stakeholders and the different activities involved have been listed together with their potential automation level. The results are described in D1.2 Workshop reports.
A first prototype of HMI fostering human automation teaming through a clear division of tasks between AI and human operator has been designed. It has been reviewed with ATCOs in the second validation session. Different potential designs to represent AI results to ATCO on A-SMGCS radar image and allow interaction over it have also been started. The work is still in progress. The HMI will be validated in the final validation session and reported in D3.1 - Specification of interaction for gradual automation programming from ATC supervision tool.
TUD has started the implementation of use case on normal operations with rescheduling on Amsterdam Schiphol. ENAC and TUD have defined the architecture and data exchanges to get the scenarios work both on Amsterdam Schiphol and Roissy-CDG airports. In addition, TUD has setup fast time simulations to validate some technical criteria such as airport capacity impact.
A first prototype of HMI fostering human automation teaming through a clear division of tasks between AI and human operator has been designed. It has been reviewed with ATCOs in the second validation session. Different potential designs to represent AI results to ATCO on A-SMGCS radar image and allow interaction over it have also been started. The work is still in progress. The HMI will be validated in the final validation session and reported in D3.1 - Specification of interaction for gradual automation programming from ATC supervision tool.
TUD has started the implementation of use case on normal operations with rescheduling on Amsterdam Schiphol. ENAC and TUD have defined the architecture and data exchanges to get the scenarios work both on Amsterdam Schiphol and Roissy-CDG airports. In addition, TUD has setup fast time simulations to validate some technical criteria such as airport capacity impact.