Explanation of Machine-Learning Solutions in Air-Traffic Management

Abstract

Advances in the trusted autonomy of air-traffic management (ATM) systems are currently being pursued to cope with the predicted growth in air-traffic densities in all classes of airspace. Highly automated ATM systems relying on artificial intelligence (AI) algorithms for anomaly detection, pattern identification, accurate inference, and optimal conflict resolution are technically feasible and demonstrably able to take on a wide variety of tasks currently accomplished by humans. However, the opaqueness and inexplicability of most intelligent algorithms restrict the usability of such technology. Consequently, AI-based ATM decision-support systems (DSS) are foreseen to integrate eXplainable AI (XAI) in order to increase interpretability and transparency of the system reasoning and, consequently, build the human operators’ trust in these systems. This research presents a viable solution to implement XAI in ATM DSS, providing explanations that can be appraised and analysed by the human air-traffic control operator (ATCO). The maturity of XAI approaches and their application in ATM operational risk prediction is investigated in this paper, which can support both existing ATM advisory services in uncontrolled airspace (Classes E and F) and also drive the inflation of avoidance volumes in emerging performance-driven autonomy concepts. In particular, aviation occurrences and meteorological databases are exploited to train a machine learning (ML)-based risk-prediction tool capable of real-time situation analysis and operational risk monitoring. The proposed approach is based on the XGBoost library, which is a gradient-boost decision tree algorithm for which post-hoc explanations are produced by SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Results are presented and discussed, and considerations are made on the most promising strategies for evolving the human–machine interactions (HMI) to strengthen the mutual trust between ATCO and systems. The presented approach is not limited only to conventional applications but also suitable for UAS-traffic management (UTM) and other emerging applications.

Towards Autonomous Drone Racing without GPU Using an OAK-D Smart Camera

“…However, the deployment of multiple drones raises issues regarding interconnectivity, reliability, and safety [36,55]. The operation of autonomous drones may seem distant yet, especially when their solutions relying on AI face the problem of explainability for the sake of liability [56]. However, sensors such as OAK-D have the potential to showcase what can be achieved with drones and AI, whose applications will extend beyond typical aerial video/photography and precision agriculture to other domains where AI-based operation and decision-making could aid or even exhibit superior performance to that of a human, for instance, in search and rescue applications where agile and smart visual identification could rapidly find victims and safe parcel delivery through intelligent sense-and-avoid systems [34,35].…”

A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory

“…In other domains such as healthcare and criminal justice, among others, the increasing interest in AI to support high-consequence human decisions has spurred the field of XAI and User-Centric eXplainable Artificial Intelligence (UCXAI) [21]-User Centered Design (UCD) refers to the methods employed when designing systems for endusers to validate novel algorithms/working methods/interaction techniques [22][23][24][25]. This primordial aspect is yet to be fully assessed in ATM, but the interest is growing [26,27].…”

“…In our review, the algorithm belonging to the Prediction category is the only one where explainability is approached clearly even if it is secondary to the papers main goals and it was restricted mainly to predicting an indicator of the trajectory, i.e., the landing time [138] and take-off time [37]. The main goal of Xie et al [27] was to explain the results of their risk of incidents and accidents predictive model. Although those AI algorithms are already useful to the ATM community, fully understanding the underlining reasons of congestion, trajectory routes, and delay, i.e., answering "why" and "why not" questions, is more than required to better enhance latter traffic, or more simply better predict it.…”

In-Flight Tests of Intruder Detection Vision System

“…The official document [1] presents the roadmap for the integration process of both manned and unmanned air traffic in the European airspace. Some practical possibilities in this field were presented in several scientific and research papers [2][3][4][5]. The authors of [2] propose using the standard ADS-B (Automatic Dependence Surveillance-Broadcast) systems for RPAS (Remotely Piloted Aircraft Systems) separation.…”

“…The authors of [2] propose using the standard ADS-B (Automatic Dependence Surveillance-Broadcast) systems for RPAS (Remotely Piloted Aircraft Systems) separation. The works [3,4] concern an overall approach to a problem of collision avoidance by unmanned systems. From the point of view of the subject matter presented in this article, the approach presented by the authors of [5], who are considering the use of vision systems for visual flight rules-based collision avoidance, is very interesting.…”