Research Project
HAIKU: Human AI teaming Knowledge and Understanding for aviation safety
HAIKU delivers prototypes of AI Digital Assistants and encourages human-centric-AI by developing guidance procedures, and by exploring Human-AI Teaming through prototypes.
Where: international
Where: international
Challenge
AI is already impacting aviation via Machine Learning, which can help to optimise systems and operations.
Approach
It is essential both for safe operations, and for society in general, that the people who currently keep aviation so safe can work, train and supervise these AI systems, and that future autonomous AI systems make judgements and decisions that would be acceptable to humans.
A signature element of HAIKU is the development of truly human-centric Digital Assistants, so that they ‘fit’ the way humans work.
The HAIKU project aims to design and develop prototypes of AI assistants for six use cases, to provide Human Factors design guidance for creating safe, effective, and trustworthy Digital Assistants in aviation, to conduct controlled experiments with high operational relevance, to develop innovative safety and validation assurance methods for Digital Assistants, and to deliver guidance on socially acceptable AI in safety-critical operations. Engineering will support the use cases related to Urban Air Mobility for drones and sky taxi operations, and to Airport Safety Watch. In this context, it will lead data collection and analysis while providing AI expertise in other relevant activities.
The project received co-founding from Horizon Europe programm - No. 101075332.
Results
Digital Assistants support pilots to manage accident
Intelligent assistance communication and operator acceptance
Drones management safe and efficient
Controllers support in maintaining situational awareness
Impacts
Technologies
AR MR VR (XR)
![]()
Welcome to EngX.
AI & Data
![]()
The power of big data leads services, products and processes to a higher level of "intelligence".
To know more
Recommended for you
Explore additional content associated with the topic
