  The rapid advancement of sensor technologies and artificial intelligence are
creating new opportunities for traffic safety enhancement. Dashboard cameras
(dashcams) have been widely deployed on both human driving vehicles and
automated driving vehicles. A computational intelligence model that can
accurately and promptly predict accidents from the dashcam video will enhance
the preparedness for accident prevention. The spatial-temporal interaction of
traffic agents is complex. Visual cues for predicting a future accident are
embedded deeply in dashcam video data. Therefore, the early anticipation of
traffic accidents remains a challenge. Inspired by the attention behavior of
humans in visually perceiving accident risks, this paper proposes a Dynamic
Spatial-Temporal Attention (DSTA) network for the early accident anticipation
from dashcam videos. The DSTA-network learns to select discriminative temporal
segments of a video sequence with a Dynamic Temporal Attention (DTA) module. It
also learns to focus on the informative spatial regions of frames with a
Dynamic Spatial Attention (DSA) module. A Gated Recurrent Unit (GRU) is trained
jointly with the attention modules to predict the probability of a future
accident. The evaluation of the DSTA-network on two benchmark datasets confirms
that it has exceeded the state-of-the-art performance. A thorough ablation
study that assesses the DSTA-network at the component level reveals how the
network achieves such performance. Furthermore, this paper proposes a method to
fuse the prediction scores from two complementary models and verifies its
effectiveness in further boosting the performance of early accident
anticipation.
