AcListant with Continuous Learning: Speech Recognition in Air Traffic Control (EIWAC 2019)

Increasing air traffic creates many challenges for air traffic management (ATM). A general answer to these challenges is to increase automation. However, communication between air traffic controllers (ATCos) and pilots is widely analog and far away from digital ATM components. As communication content is important for the ATM system, commands are still entered manually by ATCos to enable the ATM system to consider the communication. However, the disadvantage is an additional workload for the ATCos. To avoid this additional effort, automatic speech recognition (ASR) can automatically analyze the communication and extract the content of spoken commands. DLR together with Saarland University invented the AcListant® system, the first assistant based speech recognition (ABSR) with both a high command recognition rate and a low command recognition error rate. Beside the high recognition performance, AcListant® project revealed shortcomings with respect to costly adaptations of the speech recognizer to different environments. Machine learning algorithms for the automatic adaptation of ABSR to different airports were developed to counteract this disadvantage within the Single European Sky ATM Research Programme (SESAR) 2020 Exploratory Research project MALORCA. To support the standardization of speech recognition in ATM, an ontology for ATC command recognition on semantic level was developed to enable the reuse of expensively manually transcribed ATC communication in the SESAR Industrial Research project PJ.16-04. Finally, results and experiences are used in two further SESAR Wave-2 projects. This paper presents the evolution of ABSR from AcListant® via MALORCA, PJ.16-04 to SESAR Wave-2 projects

Early Callsign Highlighting using Automatic Speech Recognition to Reduce Air Traffic Controller Workload

The primary task of an air traffic controller (ATCo) is to issue instructions to pilots. However, the first contact is often initiated by the pilot. It is useful to have a controller assistance system, which could recognize and highlight the spoken callsign as early as possible, directly from the speech data. Therefore, we propose to use an automatic speech recognition (ASR) system to obtain the speech-to-text translation, using which we extract the spoken callsign. As a high callsign recognition performance is required, we use surveillance data, which significantly improves the performance. We obtain callsign recognition error rates of 6.2% and 8.3% for ATCo and pilot utterances, respectively, but can improve to 2.8% and 4.5%, when using information from surveillance dat

Validation of Aeolus wind products above the Atlantic Ocean

In August 2018, the first Doppler wind lidar in space called Atmospheric Laser Doppler Instrument (ALADIN) was launched on board the satellite Aeolus by the European Space Agency (ESA). Aeolus measures profiles of one horizontal wind component (i.e., mainly the west-east direction) in the troposphere and lower stratosphere on a global basis. Furthermore, profiles of aerosol and cloud properties can be retrieved via the high spectral resolution lidar (HSRL) technique. The Aeolus mission is supposed to improve the quality of weather forecasts and the understanding of atmospheric processes. We used the opportunity to perform a unique validation of the wind products of Aeolus by utilizing the RV Polarstern cruise PS116 from Bremerhaven to Cape Town in November/December 2018. Due to concerted course modifications, six direct intersections with the Aeolus ground track could be achieved in the Atlantic Ocean west of the African continent. For the validation of the Aeolus wind products, we launched additional radiosondes and used the EARLINET/ACTRIS lidar Polly XT for atmospheric scene analysis. The six analyzed cases prove that Aeolus is able to measure horizontal wind speeds in the nearly west-east direction. Good agreements with the radiosonde observations could be achieved for both Aeolus wind products-the winds observed in clean atmospheric regions called Rayleigh winds and the winds obtained in cloud layers called Mie winds (according to the responsible scattering regime). Systematic and statistical errors of the Rayleigh winds were less than 1.5 and 3.3ms-1, respectively, when compared to radiosonde values averaged to the vertical resolution of Aeolus. For the Mie winds, a systematic and random error of about 1ms-1 was obtained from the six comparisons in different climate zones. However, it is also shown that the coarse vertical resolution of 2km in the upper troposphere, which was set in this early mission phase 2 months after launch, led to an underestimation of the maximum wind speed in the jet stream regions. In summary, promising first results of the first wind lidar space mission are shown and prove the concept of Aeolus for global wind observations. © 2020 Author(s)

Characterization of 23 polymorphic SSR markers in Salix humboldtiana (Salicaceae) using next‐generation sequencing and cross‐amplification from related species

Premise of the study: We present a set of 23 polymorphic nuclear microsatellite loci, 18 of which are identified for the first time within the riparian species Salix humboldtiana (Salicaceae) using next‐generation sequencing. Methods and Results: To characterize the 23 loci, up to 60 individuals were sampled and genotyped at each locus. The number of alleles ranged from two to eight, with an average of 4.43 alleles per locus. The effective number of alleles ranged from 1.15 to 3.09 per locus, and allelic richness ranged from 2.00 to 7.73 alleles per locus. Conclusions: The new marker set will be used for future studies of genetic diversity and differentiation as well as for unraveling spatial genetic structures in S. humboldtiana populations in northern Patagonia, Argentina.EEA BarilocheFil: Bozzi, Jorge Alfredo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Liepelt, Sascha. University of Marburg. Faculty of Biology. Conservation Biology Group; AlemaniaFil: Ohneiser, Sebastian. University of Marburg. Faculty of Biology. Conservation Biology Group; AlemaniaFil: Gallo, Leonardo Ariel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Marchelli, Paula. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Leyer, Ilona. University of Marburg. Faculty of Biology. Conservation Biology Group; Alemania. University of Geisenheim. Institute of Botany, Plant Ecology and Nature Conservation; AlemaniaFil: Ziegenhagen, Birgit. University of Marburg. Faculty of Biology. Conservation Biology Group; AlemaniaFil: Mengel, Christina. University of Marburg. Faculty of Biology. Conservation Biology Group; Alemani

Brain\u2013Computer Interface-Based Adaptive Automation to Prevent Out-Of-The-Loop Phenomenon in Air Traffic Controllers Dealing With Highly Automated Systems

Increasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo's mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called "Vigilance and Attention Controller", a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human-machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA

ATTENTION: TARGET AND ACTUAL – THE CONTROLLER FOCUS

The main task of an air traffic controller (ATCO) is to ensure safe and efficient air traffic control (ATC). Therefore, the ATCO needs to have his/her attention at the right place at the right time on the controller working position’s displays. This will be even more challenging in the future with increasing information diversity, growing levels of automation, more complex air traffic mix, new technologies, and bigger screens. However, to deal with these challenges an attention guiding assistance system is developed to support the ATCO. This system needs to determine the area of target attention due to relevant upcoming ATC events. It should also evaluate the current area of attention as a function of the ATCO's gaze, e.g., via eye-tracking, and evaluate it. If there is a mismatch between target and actual area of attention, the attention focus of the ATCO has to be appropriately guided to relevant areas via cues. Based on an analysis of attention and situation awareness, attention guidance mechanisms have been developed and successfully validated in human-in-the-loop trials. ATCOs felt well-supported by visual non-intrusive guidance cues and even wanted to have such functionality in today’s working positions

Brain–Computer Interface-Based Adaptive Automation to Prevent Out-Of-The-Loop Phenomenon in Air Traffic Controllers Dealing With Highly Automated Systems

International audienceIncreasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo’s mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called “Vigilance and Attention Controller”, a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human–machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA