  Traffic accident anticipation is a vital function of Automated Driving
Systems (ADSs) for providing a safety-guaranteed driving experience. An
accident anticipation model aims to predict accidents promptly and accurately
before they occur. Existing Artificial Intelligence (AI) models of accident
anticipation lack a human-interpretable explanation of their decision-making.
Although these models perform well, they remain a black-box to the ADS users,
thus difficult to get their trust. To this end, this paper presents a Gated
Recurrent Unit (GRU) network that learns spatio-temporal relational features
for the early anticipation of traffic accidents from dashcam video data. A
post-hoc attention mechanism named Grad-CAM is integrated into the network to
generate saliency maps as the visual explanation of the accident anticipation
decision. An eye tracker captures human eye fixation points for generating
human attention maps. The explainability of network-generated saliency maps is
evaluated in comparison to human attention maps. Qualitative and quantitative
results on a public crash dataset confirm that the proposed explainable network
can anticipate an accident on average 4.57 seconds before it occurs, with
94.02% average precision. In further, various post-hoc attention-based XAI
methods are evaluated and compared. It confirms that the Grad-CAM chosen by
this study can generate high-quality, human-interpretable saliency maps (with
1.23 Normalized Scanpath Saliency) for explaining the crash anticipation
decision. Importantly, results confirm that the proposed AI model, with a
human-inspired design, can outperform humans in the accident anticipation.
