  We introduce the first audio-visual dataset for traffic anomaly detection
taken from real-world scenes, called MAVAD, with a diverse range of weather and
illumination conditions. In addition, we propose a novel method named AVACA
that combines visual and audio features extracted from video sequences by means
of cross-attention to detect anomalies. We demonstrate that the addition of
audio improves the performance of AVACA by up to 5.2%. We also evaluate the
impact of image anonymization, showing only a minor decrease in performance
averaging at 1.7%.
