Unsafe Maneuver Classification From Dashcam Video and GPS/IMU Sensors Using Spatio-Temporal Attention Selector

In this paper, we propose a novel deep learning architecture to classify unsafe driving maneuvers from dashcam and IMU data. Such architecture processes the output of an object detection algorithm in combination with raw video frames and GPS/IMU data. At the core of the architecture there is a novel Spatio-Temporal Attention Selector (STAS) module, which (1) extracts features describing the evolution of each object in the scene over time and (2) leverages multi-head dot product attention to select the relevant ones, i.e., the dangerous ones or the ones in danger, to perform classification. We also introduce a simple but effective methodology to increase the benefit of fine-tuning the backbone network. Our method is shown to achieve higher performance than other approaches in the literature applying attention over single frames.

Automated summary

In this paper, a novel deep learning architecture is proposed to classify unsafe driving maneuvers using dashcam and IMU data. The architecture combines object detection algorithm output with raw video frames and GPS/IMU data, and utilizes a Spatio-Temporal Attention Selector (STAS) module to extract and select features for classification. Experimental results show that the proposed method outperforms other approaches applying attention over single frames.