Attention-Based Auto-Encoder Framework for Abnormal Driving Detection

Jing Liu, Yang Liu, Donglai Wei, Wei Ni, Xinhua Zeng, Liang Song

Abstract: With the popularity of smartphones, abnormal driving detection via smartphone sensors has been proposed in recent years. However, existing methods are insufficient in exploring feature extraction, so the practical value is limited due to the low accuracy. To address this problem, we propose an attention-based auto-encoder framework for abnormal driving detection that combines the advantages of bi-directional long short-term memory and self-attention. Specifically, these two modules are embedded in the auto-encoder for modeling latent vector and exploring the internal correlations of spatial-temporal features, respectively, so as to improve the capability of reconstructing driving time series using small and representative features. We conduct experiments on the real-world datasets, and the results show that the proposed framework achieves significant performance with recall and F1-score of 96.2% and 95.0%, superior to the other baselines.