# Automatic Roadway Features Detection with Oriented Object Detection
## 6 Conclusions and Future Work  
Different roadway safety features may have a direct impact on safety. Extensive research has been conducted towards quantifying this impact. The most essential stage in the process of roadway safety management is data collection that make the process expensive and time-consuming, especially in developing and low-income countries, where the issues of roadway safety is exacerbated. Therefore, the automation of roadway safety features detection process is a promising and much-needed application. In this research, a new pervasive system for automatic road safety features detection is proposed to make the process more applicable and less costly. Given a front-facing camera and a GPS sensor, the system automatically evaluates ten roadway safety features. A modification to the state-of-the-art deep learning-based oriented object detection model is introduced, which improved its accuracy by 16.9% of mAP. Additionally, different oriented object detection strategies are presented and analyzed. The system includes a rule-based module that processes the object detection output to provide ten roadway safety features. The rule-based module does not guarantee that an absent road feature causes the estimated road segment safety score to be very low and is geographically dependent, further research work can be focused upon improving it.  
The system can be used to pervasively enable or disable autonomous driving (AD) or advanced driver assistance systems (ADAS) and could aspire for \"safety-based navigation\" applications where travelers can choose not only faster and shorter routes, but also safer routes. To train and validate the proposed approach, a dataset for roadway safety features extraction in developing countries is introduced: Roadway Safety Dataset. The dataset is composed of 473 km of driving, covering local roads, primary collector roads, primary and secondary arterial roads, and regional and primary freeways. The proposed approach is extensively evaluated against the dataset, and encouraging results are achieved that is presented as a benchmark. The average roadway safety feature prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. Finally, topological maps over GCR showing the proposed system automatic detection of each roadway safety feature are presented and analyzed. The resulted maps demonstrate the effectiveness of the proposed approach by showing areas that have safety issues to make decision makers focus on the features that need more development.  
While this research attempted to answer specific questions, it triggered even questions with abundant room for extensions and future work. Firstly, our solution can be used in ranking road safety according to the detected safety-related features using multi-criteria decision-making methods in case of lack of crash and safety data. Secondly, applying camera calibration can allow more accurate estimation of geometric dimensions of the detected road objects. Additionally, the system could aspire for \"safety-based navigation\" applications where travelers can choose not only faster and shorter routes, rather safer routes.  
Conceptualization, H.A., H.M.E. and D.S.; methodology, H.M.E. and M.N.M.; software, H.M.E., K.S. and O.R.E.; validation, H.M.E., K.S. and O.R.E.; investigation, H.A., H.M.E. and D.S.; resources, H.A. and H.M.E.; data curation, K.S.; writing--original draft preparation, H.M.E. and K.S.; writing--review and editing, H.A. and D.S.; visualization, H.M.E. and K.S.; supervision, H.A., D.S. and M.N.M.; project administration, H.A. and D.S.; funding acquisition, H.A. and H.M.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.