# Automatic Roadway Features Detection with Oriented Object Detection
## 1 Introduction  
Despite roadway safety and vehicle design improvements, the total number of fatal crashes still increases. The latest Global Status Report of the World Health Organization (WHO) reported an estimated 1.35 million annual deaths due to road traffic crashes worldwide [1]. Extensive research has been conducted to quantify the effect of roadway features on safety in the last two decades. Roadway features data collection and analysis is, not only an expensive and time-consuming process, but should also be carried out periodically to ensure properly maintaining roadway conditions. Therefore, automating roadwayfeatures detection is a promising application to contribute to the body of roadway safety management efficiently and less costly, by harnessing the potential of modern artificial intelligence (AI) techniques.  
In the last decade, a dramatic increase in computational power is witnessed, along with an abundant and growing amount of big data became available; the collective sum of the world's data volume is expected to grow from 33 Zettabytes this year to a 175 Zettabytes by 2025, for a compounded annual growth rate of 61% [2]. The field of AI greatly benefited from such two achievements to successfully train deeper, i.e., more complex, machine learning models that can learn efficient useful representations from data. That improvement is a by-product of learning feature maps [3] rather than hand-crafting them in traditional computer vision practices [4]. This led AI to exceed the human performance in some tasks like image classification, and to provide accurate object detection models from images [5]. Modern AI success can be a key enabler to the automation of roadway safety features detection.  
The objective of this paper is to harness the potential of AI computer vision to automate the process of roadway safety features detection, as a step aiming to make the process more applicable and less costly. An oriented bounding box object detection model is introduced; it presents a modified version of a state-of-the-art deep learning-based object detection method. The model solves an orientation encoding discontinuity problem to improve deep learning-based oriented object detection. The model then feeds detections to a proposed rule- based roadway safety features detection module. The input to the proposed system is an image sequence generated from a vehicle-mounted front-facing camera and the GPS information, and the output is a list of ten essential safety features. Briefly, in a single drive, the proposed architecture attempts to automatically score roadway safety features without relying on expensive data collection, management, and analysis resources.  
The automatic roadway safety features assessment can be used to pervasively enable or disable autonomous driving (AD) or advanced driver-assistance systems (ADAS). In case of connectivity, connected vehicles (CV) would send and receive estimated safety features and could send alerts to drivers about black spots or relatively less-safe segments or roads. Black spots identification is based on when the detected safety features scores are considerably lower than the roadway safety standards. Additionally, the proposed system could aspire for \"safety-based navigation\" applications where travelers can choose not only faster and shorter routes, but also safer routes. To validate the proposed approach, a case study dataset for roadway safety features extraction is introduced. The Greater Cairo Region (GCR) is the largest megacity in the Middle-East and North Africa (MENA) region [6]. The dataset covers 473 km travelled, between local roads, collector roads, regional and primary arterial roads, and regional and primary freeways. The dataset is composed of front-facing camera geotagged images fully annotated with object oriented bounding boxes and roadway safety features. The validation of the proposed approach is conducted on the test dataset, and promising results are achieved and reported as a baseline.