Use of 5G and mmWave radar for positioning, sensing, and line-of-sight detection in airport areas

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Aircraft Dynamic Rerouting Support

In the frame of Clean Sky 2 JU, the HARVIS (Human Aircraft Roadmap for Virtual Intelligent System) project introduces a cockpit assistant committed to help the pilot to reroute the aircraft in single-pilot operations. A relevant scenario for this AI assistant is that in which diversion to alternate airfield is required after an emergency. Another interesting scenario is the anticipation of radar vectors in the arrivals with time enough to safely configure the aircraft for the descent. A demonstrator is being developed for this second scenario in the context of Project HARVIS (www.harvis-project.eu). Diversion is often required after system failure, medical emergency, or just for weather phenomena (dense fog, storms, etc.) in the approaching. During regular operation if a diversion is needed the pilot in command and first officer discuss on the multiple options they have and try to find out the one they think is the best. The AI assistant will take into account characteristics of nearby airports, METAR at destination, and facilities to take care of passengers, among other factors. It may then consider several options, assess the risks and benefits of each one, and finally inform the pilot accordingly. In this scenario, the digital assistant takes care of the Options and Risks in a FORDEC procedure

Toward a Non Stabilized Approach assistant based on human expertise

97% of Non-Stabilized Approach (NSA) are continued until landing going against Standard Operational Procedures (SOP). For some of these approaches, the reason is a lack of situation awareness for others it is because of operational constraints that standard SOP do not take into account like ATC, remaining fuel on board, weather… Most of the time everything goes well but pilots often admit afterwards that they should have go-around and that safety margins were greatly reduced

User Evaluation of Conversational Agents for Aerospace Domain

The aerospace industry can benefit from conversational agents that provide efficient solutions for safety-of-life scenarios. This industry is characterized by products and systems that require years of engineering to achieve optimal performance within complex environments. With recent advances in retrieval and language models, conversational agents can be developed to enhance the system’s question-answering capabilities. However, evaluating the added-value of such systems in the context of industrial applications, such as pilots in a cockpit, can be challenging. This article presents the design, implementation, and user evaluation of a conversational agent called Smart Librarian, which is tailored to the aerospace domain’s specific requirements to support pilots in their tasks. Our results based on a controlled user experiment with flight school students indicate that the user’s perception of the usefulness of the system in completing the search task is a good predictor of both task score and time spent. The system’s responses to the relevance of the topic is also a good predictor of task score. The perceived difficulty of the search task and its interaction with the relevance of the system’s responses to the topic also play a key role in search performance. The mixed-effects models constructed in this study had large effect sizes, demonstrating participants’ ability to assess their performance accurately. Nevertheless, user satisfaction with the system’s responses may not be a reliable predictor of user search performance. Implications for the design of conversational agents based on the domain’s specific requirements are discussed

Use of 5G and mmWave radar for positioning, sensing, and line-of-sight detection in airport areas

International audienceThis paper explores innovative low-cost technologies, widely used outside of Air Traffic Management (ATM), for use in airport surface surveillance. These technologies consist of a 5G-signal-based surveillance solution and a millimeter wave (mmWave) radar augmented with artificial intelligence (AI). The 5G solution is based on the combination of 3D Vector Antenna, innovative signal processing techniques, and hybridization techniques based on time-of-arrival and angle-ofarrival estimates with uplink and downlink 5G signals, as well as Machine Learning (ML)-based Line of Sight (LOS) detection algorithms. The mmWave solution is based on mmWave radar for non-cooperative target's positioning and sensing, combined with deep learning for objects classification. Standalone 5G positioning accuracy reaches m-level accuracy in LOS scenarios and it is better with downlink reference signals than with uplink ones, while it deteriorates quite drastically in NLOS scenarios. LOS detection accuracies above 84% average accuracy can be achieved with ML. The mmWave radar is tested in different scenarios (short, medium and long range) and it provides cost-effective surface surveillance up to few hundred meters (depending on the object radar cross section RCS) with ±60°field of view. The work is being conducted within the H2020 European-funded project NewSense and it delves into the 5G, Vector Antennas, mmWave, and ML/AI capabilities for future ATM solutions