  Distracted drivers are more likely to fail to anticipate hazards, which
result in car accidents. Therefore, detecting anomalies in drivers' actions
(i.e., any action deviating from normal driving) contains the utmost importance
to reduce driver-related accidents. However, there are unbounded many anomalous
actions that a driver can do while driving, which leads to an 'open set
recognition' problem. Accordingly, instead of recognizing a set of anomalous
actions that are commonly defined by previous dataset providers, in this work,
we propose a contrastive learning approach to learn a metric to differentiate
normal driving from anomalous driving. For this task, we introduce a new
video-based benchmark, the Driver Anomaly Detection (DAD) dataset, which
contains normal driving videos together with a set of anomalous actions in its
training set. In the test set of the DAD dataset, there are unseen anomalous
actions that still need to be winnowed out from normal driving. Our method
reaches 0.9673 AUC on the test set, demonstrating the effectiveness of the
contrastive learning approach on the anomaly detection task. Our dataset, codes
and pre-trained models are publicly available.
