Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

The World Health Organization (WHO) estimated that worldwide road traffic crashes account for 1.35 million annual deaths in 2018. Most studies in road safety field have focused on studying road features effect on crash frequency and injury severity. However, limited studies use automatic road features detection methods to evaluate road safety. This research applies deep learning object detection to automatically identify roads features and Full Consistency Method (FUCOM) to rank the effect of road features on safety. A deep artificial network object detector is trained to efficiently detect road objects in a front-facing camera image, then FUCOM is applied to determine safety weights for ten Key Safety Indicators (KSIs). After determining final weights, the proposed approach is applied on a case study in Greater Cairo, in which 354 km of multi-class roads are divided to 1-km sections and assigned a road safety score (RSS). The resulting RSS identifies areas that exhibit low safety scores, enabling decision makers to focus their efforts on features that can attain highest improvement. This research contributes to supporting governments and roadway asset management agencies in prioritizing their efforts (and funding) in addressing roadway safety – especially where structured historical data collection programs are largely missing.