Smart Approach Monitoring with EPilots: Preventing Hard Landings

Smart Approach Monitoring with EPilots: Preventing Hard Landings

Mr. Dr. Atul Kumar Ramotra,Baswa Shruthika,Sneha Surampally,Koppula Manikanta,Sama Vaishnavi

Assistant Professor of Department of CSE (AI & ML) of ACE Engineering College, India.

Students of Department of CSE (AI & ML) of ACE Engineering College, India.

ABSTRACT

Hard landing remains one of the most persistent and critical safety concerns in aviation, carrying implications that extend beyond structural stress on aircraft to operational delays, increased maintenance costs, and, most importantly, passenger discomfort and safety risks. The final approach phase of flight is inherently high-stakes: pilots must continuously manage multiple, interdependent parameters such as approach speed, descent rate, altitude, wind conditions, runway surface state, aircraft weight, and control inputs. Small deviations in any of these factors can compound rapidly, creating conditions that increase the likelihood of a hard landing. Traditional aviation safety systems, including Ground Proximity Warning Systems (GPWS) and post-flight Flight Data Monitoring (FDM), rely predominantly on threshold-based rules, offering alerts only after certain parameters are exceeded. While these systems provide essential warnings, they often act reactively rather than proactively, leaving limited time for corrective pilot action in the final critical moments before touchdown.

In this study, we propose Smart Approach Monitoring with ePilots, an integrated framework that combines multivariate flight parameter analysis with Convolutional Neural Network (CNN)-based predictive modeling to assess landing stability in real-time. The framework continuously evaluates the aircraft’s approach trajectory, descent profile, and environmental factors, generating a dynamic risk score and delivering adaptive, pilot-centric advisory alerts. Unlike conventional threshold-based systems, this model considers the interactions between multiple parameters simultaneously, enabling early detection of potentially hazardous landing conditions and providing actionable recommendations for speed adjustment, flare maneuvering, or go-around decisions.

Scenario-based evaluations under dry, wet, and icy runway conditions demonstrate the robustness of the proposed system. The CNN model successfully identifies high-risk landing scenarios while minimizing false alarms, highlighting its potential for real-world operational deployment. Beyond predictive accuracy, the system emphasizes human-centric safety, enhancing situational awareness, reducing cognitive load on pilots, and supporting confident decision-making during the high-stress final approach phase.

The proposed framework represents a significant advancement in AI-assisted aviation safety, bridging the gap between automated prediction and pilot action. By combining predictive intelligence with proactive advisory mechanisms, Smart Approach Monitoring with ePilots offers a practical, operationally relevant solution that enhances landing safety, passenger comfort, and airline efficiency, while reinforcing the critical role of human judgment in modern cockpit operations.

Keywords: Hard landing prediction, Smart approach monitoring, Convolutional Neural Network, Aviation safety, Decision support system.