  Traffic accident anticipation aims to predict accidents from dashcam videos
as early as possible, which is critical to safety-guaranteed self-driving
systems. With cluttered traffic scenes and limited visual cues, it is of great
challenge to predict how long there will be an accident from early observed
frames. Most existing approaches are developed to learn features of
accident-relevant agents for accident anticipation, while ignoring the features
of their spatial and temporal relations. Besides, current deterministic deep
neural networks could be overconfident in false predictions, leading to high
risk of traffic accidents caused by self-driving systems. In this paper, we
propose an uncertainty-based accident anticipation model with spatio-temporal
relational learning. It sequentially predicts the probability of traffic
accident occurrence with dashcam videos. Specifically, we propose to take
advantage of graph convolution and recurrent networks for relational feature
learning, and leverage Bayesian neural networks to address the intrinsic
variability of latent relational representations. The derived uncertainty-based
ranking loss is found to significantly boost model performance by improving the
quality of relational features. In addition, we collect a new Car Crash Dataset
(CCD) for traffic accident anticipation which contains environmental attributes
and accident reasons annotations. Experimental results on both public and the
newly-compiled datasets show state-of-the-art performance of our model. Our
code and CCD dataset are available at https://github.com/Cogito2012/UString.
