Two-stream neural architecture for unsafe maneuvers classification from dashcam videos and GPS/IMU sensors

In this paper, we propose a novel deep learning architecture for the end-to-end classification of unsafe maneuvers from dashcam data; the proposed model is based on an innovative two-stream architecture capable of processing both video and GPS/IMU signals as input streams. A wide experimentation on a well known naturalistic driving dataset (SHRP2 NDS) shows that the two sources of information complement each other in the classification task and proves the effectiveness of the proposed approach. As a by-product of this research, we propose and make available a novel classification of safety-critical events based on the unsafe maneuver leading to them, which is representative of the real distribution of car crashes and near crashes.