In F1 vehicles, Aero-elastic wing flutter is a serious concern on the integrity of the wing structure that can cause catastrophic failure in the vehicle within milliseconds of the onset of flutter, at race speeds above 300 km/h. The current telemetry-approaches based on convolutional neural networks (CNNs) or long short-term memory (LSTM) models show limited predictions lead time and high false-alarm rates under the highly dynamic aero-mechanical conditions they encounter when handling race events. This paper introduces the Wing Flutter Detection (TF-WFD) framework based on Transformer, combining high-speed vision and multiple sets of aero-elastic parameters from inertial measurement units (IMUs) and fibre Bragg grating (FBG) strain gauge data.This paper proposes a framework of Wing Flutter Detection (TF-WFD) based on Transformer, which integrates heterogeneous multi-sensor telemetry data, including a high-speed vision system, IMUs, and fibre Bragg grating (FBG) strain meter. A real-time risk metric based on the structural deformation, acceleration, and velocity features is derived from a composite Flutter Index (FI), which is interpretable. Understanding how well the framework can perform, it achieves an accuracy of 97.9% in classification, an F1-score of 0.974 in detection, a detection lead time of 180ms (twice that of CNN baselines) and 0.05 false alarms per hour, which allows for proactive aerodynamic intervention before structural damage is imminent.
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