Enhancing Situational Awareness of Helicopter Pilots in Unmanned Aerial Vehicle-Congested Environments Using an...

Area of Science:

• Aerospace Engineering
• Artificial Intelligence
• Computer Vision

Background:

• Increasing prevalence of Unmanned Aerial Vehicles (UAVs) presents significant collision risks to helicopter operations.
• Maintaining situational awareness (SA) is critical for helicopter pilots, especially in complex airspace.
• Existing systems may not adequately address the threat posed by numerous, unpredictable UAVs.

Purpose of the Study:

• To propose an Airborne Visual Artificial Intelligence System (AVIS) to enhance helicopter pilot SA.
• To develop a system capable of detecting UAVs, estimating their distance, and predicting collision probabilities.
• To provide timely alerts to pilots regarding potential UAV collisions.

Main Methods:

• Integration of spatial and temporal deep learning models with stereo vision techniques.
• Development of algorithms for UAV detection and depth estimation.
• Implementation of a collision prediction model and pilot alert system.

Main Results:

• Demonstrated feasibility of an AI-driven system for improving SA in UAV-congested environments.
• Successful estimation of UAV depth and prediction of potential collisions.
• Generation of pilot alerts for high-probability collision scenarios.

Conclusions:

• The proposed Airborne Visual Artificial Intelligence System (AVIS) effectively enhances helicopter pilot SA.
• The integration of AI and computer vision offers a viable solution for mitigating UAV-related risks.
• This technology holds potential for future autonomous aircraft applications and airspace safety.