  Accurate behavior prediction for vehicles is essential but challenging for
autonomous driving. Most existing studies show satisfying performance under
regular scenarios, but most neglected safety-critical scenarios. In this study,
a spatio-temporal dual-encoder network named STDA for safety-critical scenarios
was developed. Considering the exceptional capabilities of human drivers in
terms of situational awareness and comprehending risks, driver attention was
incorporated into STDA to facilitate swift identification of the critical
regions, which is expected to improve both performance and interpretability.
STDA contains four parts: the driver attention prediction module, which
predicts driver attention; the fusion module designed to fuse the features
between driver attention and raw images; the temporary encoder module used to
enhance the capability to interpret dynamic scenes; and the behavior prediction
module to predict the behavior. The experiment data are used to train and
validate the model. The results show that STDA improves the G-mean from 0.659
to 0.719 when incorporating driver attention and adopting a temporal encoder
module. In addition, extensive experimentation has been conducted to validate
that the proposed module exhibits robust generalization capabilities and can be
seamlessly integrated into other mainstream models.
