Advanced International Journal for Research

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E-Pilots: A Machine Learning System Deployable In The Cockpit For Predicting Early Hard Landings In Commercial Aviation

Author(s) Ms. Gunn Bansal, Mr. Tushar Gupta, Tanya Bansal, Sneha Dhyani, Dr. Satendra Kumar
Country India
Abstract Hard landings are a major and ongoing danger in commercial flights. They can cause damage to the aircraft's structure, raise maintenance costs, and even injure passengers or crew. Currently, safety regulations mainly use data collected after a flight to detect hard landings, which means problems are only identified after they occur, with no opportunity to prevent them in real time. This study introduces "E-Pilots," a new system designed for use in the cockpit. It is engineered to determine the likelihood of a hard landing while the aircraft is on approach. E-Pilots uses machine learning to analyse real-time flight parameters such as vertical speed, descent angle, and deviation from the correct flight path. The system is trained on a large volume of historical flight data, enabling it to identify subtle indicators of a potentially hazardous landing. Early results demonstrate that E-Pilots can predict hard landings with a high degree of accuracy, providing pilots with timely warnings. This would enable flight crews to modify their landing plan or execute a go-around, thereby improving flight safety. This paper explains the system architecture, the data requirements, and the methods employed. It concludes that a system such as E-Pilots could transform landing safety management and significantly reduce the frequency of hard landings.
Keywords Hard landings, Aviation safety, Real-time prediction, Machine learning, Flight data analysis, Cockpit deployable system, Predictive modeling.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-18
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5842
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