A Frontal Automotive Airbag Ignition Decision Model Based on Weakly Supervised Timing-Aware Learning

To address the difficulty of simultaneously ensuring trigger reliability and ignition-timing accuracy for frontal automotive airbags when full-vehicle crash data are expensive, positive samples are limited, and frame-level labels are unavailable, this study investigates a weakly supervised timing-aware decision method and develops a corresponding airbag ignition model. First, an optimal ignition-timing matrix for frontal crash conditions is determined from dummy head trajectories and airbag deployment characteristics measured in full-vehicle crash tests. Multichannel longitudinal body-acceleration signals are then processed by zero-phase filtering, time-origin normalization, channel alignment, and standardization. Pointwise weak labels are generated using a rule that combines an amplitude threshold, multichannel agreement, and a minimum-duration constraint. Window-level samples are constructed with a 15 ms window and a 5 ms stride, and Gaussian timing-aware weights centered on the target ignition time direct the model toward the millisecond-level critical trigger region. A two-layer Bi-LSTM extracts bidirectional temporal features, while inverse-frequency class weighting, negative-window downsampling, and amplitude-scaling and noise augmentation that preserve relative timing semantics mitigate small-sample and class-imbalance effects. During inference, the output probability sequence is coordinated with condition-specific thresholds, consecutive-window requirements, and delay parameters to produce a calibratable, interpretable, and traceable ignition strategy. Evaluation on an independent test set shows a 100% trigger rate for six frontal crash conditions and a 0% false-trigger rate for negative conditions, including misuse, low-speed, and side-impact cases. The macro-averaged absolute timing error (S_MAE) is 0.625 ms, and both R₅ and R₁₀ reach 98.81%. The mean absolute errors are 0 ms at 23, 30, 32, 35, 50 km/h and 3.75 ms at 56 km/h. Ablation results further show that removing timing-aware weighting or data augmentation, or introducing excessive temporal perturbation, markedly increases ignition-timing error; under the present small-sample weak-supervision setting, the Bi-LSTM backbone also outperforms bidirectional GRU and Temporal CNN in timing alignment. These results demonstrate that combining the optimal timing matrix, weak-label generation, timing-aware weighting, and explicit strategy coordination unifies the objectives of whether and when to trigger, enabling millisecond-level timing alignment while strictly suppressing negative-condition triggers.