Drone expansion needs to be considered as a menace in cases of negligent, illicit, or non-cooperative use. In the case of airports, a complete protection against drone intrusion should rely on an intrusion management system, aiming at avoiding the closure of the airport. This system requires the setting of proper risk assessment methodologies for airport operations, to explicitly consider the features of drone intrusion, possibly from a quantitative point of view. This work proposes a methodological framework for the risk assessment of drone intrusions in airports, tailored on drone-intrusion features, airport features, and current operations, and considering both safety-related and security-related causes. The framework is based on the combination of model-based and data-driven approaches in order to: (i) estimate an airport vulnerability index, to measure the susceptibility of the airport to drone intrusions, based on reference datasets; (ii) specify a set of event trees to evaluate the risks of the different threat scenarios related to drone intrusions. The proposed methodological framework is applied to a concrete case study, related to Milan Malpensa airport. The achieved results show the effectiveness of the approach and elicit further requirements for counter-drone systems in airports based on the assessed risks.

The use of Unmanned Aerial Systems, commonly called drones, is growing enormously today. Applications that can benefit from the use of fleets of drones and a related human–machine interface are emerging to ensure better performance and reliability. In particular, a fleet of drones can become a valuable tool for monitoring a wide area and transmitting relevant information to the ground control station. We present a human–machine interface for a Ground Control Station used to remotely operate a fleet of drones, in a collaborative setting, by a team of multiple operators. In such a collaborative setting, a major interface design challenge has been to maximize the Team Situation Awareness, shifting the focus from the individual operator to the entire group decision-makers. We were especially interested in testing the hypothesis that shared displays may improve the team situation awareness and hence the overall performance. The experimental study we present shows that there is no difference in performance between shared and non-shared displays. However, in trials when unexpected events occurred, teams using shared displays-maintained good performance whereas in teams using non-shared displays performance reduced. In particular, in case of unexpected situations, operators are able to safely bring more drones home, maintaining a higher level of team situational awareness.

Nowadays, in view of the growing traffic volume, an appropriate aircraft sequencing in the arrival sector is needed to maintain safety levels and improve the performance of the runway system and flight times. This paper presents a digital assistant supporting the air traffic controller in aircraft sequencing by providing suggestions for next waypoints, speed adjustments and altitude holdings. On the one hand, the suggested paths are such to preserve safety by ensuring the prescribed minimum separation, while also promoting environmental benefits through continuous descent operations (CDO). On the other hand, the suggestions aim to reduce landing times, improving the runway throughput. The proposed tool exploits multipath planning, for which a global optimization technique is used in conjunction with the dynamic time warping distance metric and a reinforcement learning approach to resolve conflicts through speed modulation and/or altitude holding. The performances of the assistant are assessed by means of a multi-agent simulator tailoring its reasoning on the procedures of Olbia airport (Italy). The analysis of a stream of many random aircraft has revealed its effectiveness in terms of arrival time reduction against a standard first-come-first-served strategy, usually adopted by controllers, and strong conflict reduction while considering a CDO-like adherence. Additionally, the man/machine interaction is investigated through an analysis of the overall latency from the suggestions provided by the digital assistant up to the actual aircraft maneuvers.

The drone market’s growth poses a serious threat to the negligent, illicit, or non-cooperative use of drones, especially in airports and their surroundings. Effective protection of an airport against drone intrusions should guarantee mandatory safety levels but should also rely on a resilience-driven operational concept aimed at managing the intrusions without necessarily implying the closure of the airport. The concept faces both safety-related and security-related threats and is based on the definitions of: (i) new roles and responsibilities; (ii) a set of operational phases, accomplished by means of specific technological building blocks; (iii) a new operational procedure blending smoothly with existing aerodrome procedures in place. The paper investigates the evaluation of such a resilience-driven operational concept tailored to drone-intrusion features, airport features, and current operations. The proposed concept was evaluated by applying it to a concrete case study related to Milan Malpensa Airport. The evaluation was carried out by real-time simulations and event tree analysis, exploiting the implementation of specific simulation tools and the assessment of resilience-oriented metrics. The achieved results show the effectiveness of the proposed operational concept and elicit further requirements for future counter-drone systems in airports.

The current rapid technological change identifies the evolution of people’s transportation as one of the primary effects. Hybrid-electric propulsion reveals potential advantages, including fuel savings, lower pollution, and reduced noise emissions. It is becoming a viable alternative propulsion technology for ground and marine applications and the aviation sector. Hybrid-electric propulsion systems can meet the high demands of next-generation aircraft in terms of lower operating costs, economy, and fuel efficiency while maintaining high flight performance. Introducing similar disruptive technologies requires an evolution of the traditional certification approach and associated means of compliance. Even if it starts with evaluating a hybrid propulsion system, the proposed process can also be adopted in other areas where disruptive technologies need to be adopted, such as H2 fuel systems and active wings, to summarize some potential applications. The Certification Approach for Disruptive Technologies adopts a top–down process, reversing and mixing the usual certification approach currently used for aircraft. It is based on a safety assessment fully integrated into the system’s development. The result of this process will consist of a list of gaps in certification requirements, their classification based on gap solution impact, and proposals to close those gaps.

With the U‐space revolution, drones are going to reshape both the physical space and the cyberspace of the future urban environment, also with the support of autonomy and artificial intelligence (AI). However, this revolution comes with the cost of new multi‐domain risks, which may be traced back to cyber and physical threats within drone‐based new entrants. A proper assessment and treatment of these risks is essential to achieve the safety and security objectives of U‐space for the drone ecosystem. This will entail further research, especially for the analysis of drone intruders and for the mitigation of the related U‐space impacts. This work proposes a concept for improving the U‐space resilience through a novel AI‐centric service, named DARS (drone attack resilience service), focused on managing unauthorised operations of intruder drones in the physical and cyber domains. DARS‐related threat scenarios and risk‐assessment capabilities are discussed, resorting also to modelling specific drone cyber‐physical attacks. A detailed analysis of DARS AI‐centric functional architecture is provided, with a survey of the potential approaches for intruder trajectory prediction and intent recognition, to be used for the next design stages. Lastly, the work provides a preliminary analysis of how the neutralisation functions could be implemented in DARS.