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.
Simoncini M., De Andrade D.C., Salti S., Taccari L., Schoen F., Sambo F. (2020). Two-stream neural architecture for unsafe maneuvers classification from dashcam videos and GPS/IMU sensors. Institute of Electrical and Electronics Engineers Inc. [10.1109/ITSC45102.2020.9294189].
Two-stream neural architecture for unsafe maneuvers classification from dashcam videos and GPS/IMU sensors
Salti S.;
2020
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
