  In recent days, with increased population and traffic on roadways, vehicle
collision is one of the leading causes of death worldwide. The automotive
industry is motivated on developing techniques to use sensors and advancements
in the field of computer vision to build collision detection and collision
prevention systems to assist drivers. In this article, a deep-learning-based
model comprising of ResNext architecture with SENet blocks is proposed. The
performance of the model is compared to popular deep learning models like
VGG16, VGG19, Resnet50, and stand-alone ResNext. The proposed model outperforms
the existing baseline models achieving a ROC-AUC of 0.91 using a significantly
less proportion of the GTACrash synthetic data for training, thus reducing the
computational overhead.
