SLAFusion: Attention fusion based on SAX and LSTM for dangerous driving behavior detection

Abstract

Dangerous driving behaviors are the main cause of most traffic accidents, and the detection of these behaviors is one of the extremely important researches in Intelligent Transportation System (ITS). Although some recent approaches consider dangerous driving behavior as a process containing multiple instantaneous points, which is ignored by traditional methods, they fail to take into account the loss of temporal information due to the reasonable division of instantaneous points. Therefore, this paper proposes a Symbolic Aggregate approXimation (SAX) and Long Short-Term Memory (LSTM)-based Attention Fusion method (SLAFusion) to detect dangerous driving behavior more accurately. This method evaluates various granularity of driving processes and explores the association between adjacent processes. First, a D-value Piecewise Aggregate Approximation (DPAA) is designed to model the change of vehicle state in mining driving processes with diverse granularity. Then, the features of multiple driving processes are fused to enhance each other to solve the problem of information loss due to segmentation of driving processes. Further, the auxiliary information is fused to incorporate the environmental features of the vehicle during driving. Finally, the experimental results on real datasets indicate that the proposed SLAFusion method improves the recall and F-score by 3.2% and 2.6% respectively in comparison with the existing benchmark methods.