The present application relates to the technical field of intelligent analysis and injury identification of traffic accidents, and discloses a
pedestrian-oriented repetitive brain
injury risk identification method, which comprises the following steps: constructing an accident
database containing human-vehicle and human-ground secondary collision; simulating and restoring the accident scene and extracting head
acceleration time history and
brain tissue maximum principal strain; performing time-frequency
decomposition by
wavelet packet transform and calculating
wavelet packet energy; linearly weighting and fusing the
brain tissue strain of the two collisions by taking the energy proportion as
a weighting coefficient to obtain a multi-
modal fusion index; constructing a damage
probability model based on Weibull distribution, completing parameter maximum likelihood
estimation through Bernoulli likelihood function, establishing a damage risk curve, verifying and optimizing the damage risk curve, and finally forming a practical brain
injury risk identification curve. The present application comprehensively uses
time domain,
frequency domain and strain information, quantifies the cumulative damage effect of repetitive collision, effectively improves the comprehensiveness and accuracy of
pedestrian brain injury evaluation, and is suitable for
vehicle safety design and
traffic accident injury identification.