This paper presents a Grammar-aware Driver Parsing (GDP) algorithm, with deep features, to provide a novel driver behavior situational awareness system (DB-SAW). A deep model is first trained to extract highly discriminative features of the driver. Then, a grammatical structure on the deep features is defined to be used as prior knowledge for a semi-supervised proposal candidate generation. The Region with Convolutional Neural Networks (R-CNN) method is ultimately utilized to precisely segment parts of the driver. The proposed method not only aims to automatically find parts of the driver in challenging “drivers in the wild” databases, i.e. the standardized Strategic Highway Research Program (SHRP-2) and the challenging Vision for Intelligent Vehicles and Application (VIVA), but is also able to investigate seat belt usage and the position of the driver's hands (on a phone vs on a steering wheel). We conduct experiments on various applications and compare our GDP method against other state-of-the-art detection and segmentation approaches, i.e. SDS   [1]  , CRF-RNN   [2]  , DJTL   [3]  , and R-CNN   [4]   on SHRP-2 and VIVA databases.
['T. Hoang Ngan Le', 'Chenchen Zhu', 'Yutong Zheng', 'Khoa Luu', 'Marios Savvides']
DeepSafeDrive: A grammar-aware driver parsing approach to Driver Behavioral Situational Awareness (DB-SAW)