STADAS: semi-supervised traffic anomaly detection with ambiguous samples from real-world situations

Abstract

Autonomous vehicle accidents are occurring even up to a recent date due to abnormal behavior of nearby traffic-agents. Abnormal agents are identified by observing the ambiguous behavior of the agents before the actual abnormality. Therefore, to effectively prevent accidents, models should be trained with soft labels of ambiguous situations. However, existing anomaly datasets only contain hard labels, which can not imply the ambiguity of the actual scenarios. To fully utilize the small number of hard labeled ambiguous data, we propose a simple and effective semi-supervised approach, namely STADAS. Our STADAS exploits two regulatory signals from unlabeled data. One signal is the ambiguity soft label indicating the ambiguity of the unlabeled samples which is computed from the teacher model. With our novel Ambiguity loss, our model can properly understand and handle ambiguous situations. The other is the pseudo distance label which connotes information about the dynamics of a vehicle. We show that our method has impressive anomaly detection ability quantitatively and qualitatively. Quantitatively, ours outperforms other semi-supervised methods (e.g., pseudo label, mean-teacher) regarding ROC-AUC and F1 score. Moreover, ours has fewer false negatives than the supervised model. Qualitatively, ours can detect ambiguous situations that other methods could not properly detect.