Adaptive Cockpit

Roadmap Overview

An adaptive cockpit is an advanced, AI-driven system designed to enhance aircraft safety and pilot performance during unexpected or high-stress situations. This technology integrates various sensors and intelligent algorithms to monitor both the aircraft’s status and the pilot’s physiological state in real time. Key features of an adaptive cockpit include:

1. Continuous monitoring of aircraft systems and flight parameters

2. Real-time analysis of pilot biometrics and behavior

3. Detection of sudden, potentially disorienting events or anomalies

4. Intelligent assessment of the pilot’s cognitive and emotional state

5. Provision of timely, context-aware assistance and guidance

When the system detects signs of pilot stress, confusion, or delayed responses, it intervenes with appropriate support. This may include a simplified display of critical information, biofeedback to reduce stress, or automated assistance with certain tasks. The adaptive cockpit aims to mitigate the impact of human factors such as the startle effect and cognitive overload. By providing tailored, real-time support, pilots can more quickly regain situational awareness and make informed decisions during critical moments. This technology represents a significant advancement in aviation safety, offering a proactive approach to managing complex scenarios and reducing operational risks. As it continues to evolve, the adaptive cockpit has the potential to revolutionize cockpit design and pilot training, ultimately leading to safer and more efficient flight operations.

Design Structure Matrix (DSM) Allocation

Our technology roadmap focuses on an Adaptive Cockpit (3ADC) at the subsystem level (level 3). Components of the adaptive cockpit are AI for interpreting and modeling data to determine aircraft and pilot status (4AI), Biometric Sensors for measuring pilot status indicators (4BIO), aircraft sensors for detecting aircraft status (4ACS), adaptive displays that can highlight critical information for a pilot (4ADD), a lighting system that assists pilots in maintaining respiratory rates and creating a calming atmosphere (4LIS), and the pilot responsible for flying the aircraft (4PIL). Other subsystems in the cockpit include navigation and communication systems (3NAC), and flight controls (3FLC). We have also listed other level 2 products that support our level 1 market of safe commercial aviation (1SCA).

Roadmap OPD

In the figure below, we provide an Object-Process-Diagram (OPD) of the 3ADC roadmap. This diagram captures the main object of the roadmap (Adaptive Cockpit), its main process (Teaming), the various tools required, the sub-processes required for Teaming, and the change in the status of the pilot as a result of Teaming. Additionally, we have included a zoomed-in look into the Temaing process and its four sub-processes (Detecting, Modeling, Supporting, and Augmenting).

The Object Process Language (OPL) below is the auto-generated grammatical version of the OPD above. The first OPL is for the Level 1 OPD, whereas the second OPL is for the decomposition of Teaming into its four sub-processes.

Figures of Merit

Important FOMs for 3AISC

Figure of Merit	Description	Trends	Units
Incident Rate	Rate of incidents and accidents normalized by flight time aircraft	decreasing	$\frac{\text{incidents}}{\text{passenger mile}}$
System Maintainability	How much time is spend maintaining aircraft systems, measured by comparing maintenance time per flight time	decreasing	$\frac{\text{maintenance hours}}{\text{passenger mile}}$
Response Time	Time required to identify and mitigate safety issues	flat	$\text{minutes}$
System Uptime	Amount of time aircraft is available to be dispatched on a mission as a percentage of wall time	increasing	$\text{\%}$
Cognitive Load on Crew	As measured by industry standard NASA TLX score	flat	$\text{unitless}$

Pilot Efficiency

Pilots are currently a critical part of air transportation systems and making sure they are efficiently transporting passengers is an important figure of merit (FOM) of an airline. The definition of pilot efficiency is below.

{Efficiency}_{Pilot} = \frac{{Capacity}_{Pass} * {LF}_{Pass}}{{Pilot}_{Plane}} * {Flight}_{Year}

Accident Rate

Detail of Accidents per Passenger Hour (US-based General Aviation fleet)

Detail of Accident Rates (per million flight cycle), 1959 to 2022

Embraer aicraft highlighted in green.

Alignment with Company Strategic Drivers

Positioning of Company vs. Competition

The chart below summarizes some key metrics for the major players in aircraft manufacturing. There is a clear difference in market share, with Boeing and Airbus demonstrating a duopoly over commercial aviation. Embraer and Bombardier have focused their efforts on commercial aviation's regional and business segments.

Aircraft are incredibly complex systems that impart a tremendous demand on pilots. We will look at some characteristics and traits of various aircraft and use them as a proxy for complexity. The chart below highlights the various levels of complexity in commercial aviation across segments and manufacturers.

The chart below shows that Airbus is the only major manufacturer that shows public-facing activity in RDO / SPO R&D. There is an opportunity for companies to differentiate their architectures and provide a solution that will assist the industry in meeting pilot demands in the future by providing single-pilot aircraft.

Technical Model

Financial Model

Financial payoff of R&D plan showing value of improvement in FOMs for the business.

List of R&D Projects

This will be the future R&D portfolio to enable the technology on the roadmap.

Key Publications and Patents

Publications

1. Masi, G.; Amprimo, G.; Ferraris, C.; Priano, L. Stress and Workload Assessment in Aviation—A Narrative Review. Sensors 2023, 23, 3556. https://doi.org/10.3390/s23073556

   This paper reviews various workload and stress assessment tools used within the military and civilian aviation sector. Stress and workload are key indicators of pilot performance, but measuring these conditions is not straightforward. Some of the highlighted indicators used for determining stress and workload are neurophysical (heart rate (HR) and heart rate variability (HRV)), respiratory rate, electrodermal activity (EDA), body temperature, eye movements and dilation, etc. Subjective measures such as the NASA-TLX can also be used to determine workload. One of the major conclusions was that there is no positive indicator for stress and workload in pilots. All the indicators mentioned above are only potential indicators for stress, so a comprehensive approach must be taken to model the status of the pilot. Most of the studies reviewed in this analysis used a combination of assessments to deduce stress and workload. The most common neurophysical used in the studies was HR and HRV. The authors comment on the feasibility of taking these measurements during flight, recommending that the devices should be light and not encumber the pilot's ability to function by using single-lead wearables or smart devices.

   The authors suggest that more research should be done on eye tracking, near-infrared spectroscopy, grip strength during flight, and other parameters. Lastly, the author recommends further research into adaptive cockpits.

   This review is an excellent source of background science regarding safety in aviation as it relates to pilot performance. The review's conclusion suggests that a combination of assessments is required, that there is still work to do to establish a reliable model for pilot stress and workload, and that adaptive cockpits would be the potential practical application of these findings.

2. Li, Qinbiao & Chen, Chun-Hsien & Ng, Kam K.H. & Yuan, Xin & Yiu, Cho Yin. (2024). Single-pilot operations in commercial flight: Effects on neural activity and visual behaviour under abnormalities and emergencies. Chinese Journal of Aeronautics. 37. 10.1016/j.cja.2024.04.007.

   This publication is a thesis that focuses on enhancing flight safety through neurophysical analysis and mental state predictions in single-pilot operations (SPO). As the airline industry seems primed to shift from dual-pilot operations (DPO) to SPO, a new method for sustaining safe flights must be adopted. This thesis aims to develop a system to monitor pilots and accurately identify off-nominal statuses in real time. The researcher performed various experiments on trained pilots in simulators. They collected neurophysiological data such as EEG and eye movements. In some experiments, the scenario was transitioned from DPO to SPO to investigate how the neurophysiological data changes. The data was then cleaned and analyzed. The primary finding reported here is that using, analyzing, and modeling this data type is a valid way to identify risky flight behaviors. The researcher claims an accuracy of up to 92.86%. A few other conclusions worth mentioning are that visual behaviors were pronounced during SPO, meaning the pilots were fixated on single points, especially during emergencies. Additionally, human error was more likely during SPO, and pilots reported feeling more stressed.

   This thesis directly supports the need for the technology we are roadmapping. The author provides the context surrounding the shift to SPO, and the risks associated with this transition. Additionally, the research suggests it is feasible to implement the strategy our technology demonstrates.

Patents

1. Eye Gaze Tracking – US Patent No. US11899837B2 – CPC G06F

   The Eye Gaze patent shows a new way to detect and track eye gaze. The primary components of the patent are a visible wavelength camera, an infrared camera, and processors. It works by using the IR cameras to develop a 3D point cloud of a person’s face. It also creates a 2D image of the face with the visible wavelength camera. The processors take this data and create symmetry planes, which can be used to determine where the person is looking.

   This technology is relevant because the publication review suggested that eye-tracking is a great indicator of pilot status. However, wearing devices that obstruct vision or could bounce around during flight is not ideal. This technology addresses both of those concerns.

2. Smart Ring System for Monitoring Sleep Patterns And Using Machine Learning Techniques To Predict High-Risk Driving Behavior – US Patent No. US12077193B1– CPC B60W

   The patent above is for a smart ring that monitors sleep patterns in users and predicts high-risk behaviors through machine learning. The device is a ring, similar to an Oura ring, that tracks HR/HRV, body temperature, motion, etc. This data can be used in conjunction with vehicle data to predict high-risk behavior.

   While this patent is centered around sleep and performance of an automobile driver, the general concept can be applied in other domains. It shows that small, non-obstructive devices can be used to collect data, transmit data, and inform models that could improve people's performance in control of transportation systems.

3. Systems And Methods for Monitoring Pilot Health – US Patent No. US20190090800A1– CPC A61B

   This patent outlines a system for monitoring the health of a pilot and status of the aircraft. It also contains a way to analyze the pilot status and interface with them. Lastly, this patent also includes an actuator to take control of the aircraft if necessary.

   This patent is incredibly relevant as it is nearly identical to our technology with a few exceptions. The main difference is our technology aims to provide biofeedback to get the pilot back into normal operating conditions. In contrast, this patent utilizes a robot-pilot to manually control the aircraft in an emergency. The process up until that point is nearly identical.

Related Projects

• Haiku Project - Use Case 1 - Intelligent Assistant

• Embraer Pulse Concept Jet

• Aerospace Testing International - Building an autonomous business jet

Technology Strategy Statement

Concluding narrative summary of roadmap with swoosh chart showing future growth.