The Controller Working Position (CWP) is the centre of the human-machine interactions for Air Traffic Controllers (ATCos). The tools and platforms used by air traffic controllers shape and inform their way of operating, their mental models and ultimately their performance. This in turn promotes the safe and efficient management of air traffic. Voice communications between ATCos and pilots is still not digitalised and, therefore, not accessible for machine analysis.
To change the current status, the SESAR Master Plan vision includes increasing digitalisation and automation of Air Traffic Control (ATC). Digitization of ATCo-pilot communication by Automatic Speech Recognition (ASR) is a cornerstone. Data communication, i.e. Controller Pilot Data Link Communication (CPDLC) or text-based transmission of data and ASR are no competing, but complementary. ASR can e.g. replace ATCos mouse-based inputs into the assistant systems. Voice is the most efficient way of human-human communication. Why not benefitting from ASR in human-machine communication? Recently, solution PJ.16-04 of SESAR 2020 Wave-1 has demonstrated that the current ASR systems are mainly targeting the every-day consumer market and are not suited to safety and time critical application areas such as Air Traffic Management (ATM). As shown in the past, trust and acceptance by ATCos of low fidelity tools is an obstacle for deployment.
Nevertheless, several past projects have indicated the usefulness and utility of ASR: (i) AcListant® quantified the benefits with respect to workload reduction and performance increase, (ii) MALORCA project demonstrated commercial viability and enhanced models, (iii) SESAR 2020 solution PJ.16-04 explored industrial integration and requirements building.
So far, these precursor projects have focused on research and development of a set of models, further deployed in relatively simple and controlled application domains. The HAAWAII project proposes to target complex and challenging environments and, more importantly, wider applications of automatically recognized voice communications. More specifically, HAAWAII proposes two general objectives:
1. Research and develop data-driven (machine learning oriented) approaches to be deployed for novel and complex environments from two large ANSPs, demonstrating an increased validity of the tools;
2. Demonstrate the wider applicability of the tools in ATM, focussing on generating benefits for the ATCos and the ANSPs, i.e. reducing workload, increasing both efficiency and safety.
Overall, HAAWAII intends to focus on the following applications:
• Pilot readback error detection,
• Modelling and being able to anticipate controller behaviour,
• Pre-filling radar labels and CPDLC messaging using the automatic speech recognition,
• Human performance metric extraction.
