# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
###### Abstract  
The state of roads may sometimes be difficult to perceive due to intense climate conditions, absence of road signs, or simply human inattention, which may be harmful to both vehicles and drivers. The automatic monitoring of the road states represents a promising solution to warn drivers about the status of a road in order to protect them from injuries or accidents. In this paper, we present a novel application for data collection regarding road states. Our application entitled \"Road Scanner\" allows onboard users to tag four types of segments in roads: smooth, bumps, potholes, and others. For each tagged segment the application records multimodal data using the embedded sensors of a smartphone. The collected data concerns mainly vehicle accelerations, angular rotations, and geographical positions recorded by the accelerometer, the gyroscope, and the GPS sensor, respectively, of a user phone. Moreover, a medium-size dataset was built and machine learning models were applied to detect the right label for the road segment. Overall, the results were very promising since the SVM classifier (Support Vector Machines) has recorded an accuracy rate of 88.05%. smartphone sensors; road scanning; data collection; in situ labeling; machine learning +
Footnote †: journal:  
be used to justify the use of machine learning techniques. First, machine learning can aid in the efficient and effective handling of vast amounts of data. Furthermore, when making predictions or classifying data, machine learning algorithms can achieve significantly higher accuracies than humans. Finally, machine learning can aid in the discovery of patterns and correlations in data that might otherwise go undetected. Since there is an absence of a road surface scanning feature from well-known applications, such as \"google maps\" or \"here we go\", our motivation in this research is to provide--as a first step--a novel smartphone application called \"Road Scanner\". This route application allows onboard users to identify four types of road segments: smooth, bumps, potholes, and others. For each tagged segment, the application records multimodal data using the built-in smartphone sensors namely the accelerometer, gyroscope, and GPS. To test the developed application, a dataset was collected and merged with another online dataset to serve as a benchmark for classification approaches. Indeed, several machine learning models were applied to map the collected sensor data to the right route state. The proposed application represents a great tool for researchers to gather rich data on the ground and develop machine learning algorithms. Our technique in this paper has numerous advantages. In fact, the produced program was built on cross-platform technology, making it compatible with all devices (Android and iOS). Furthermore, the application's architecture enables efficient handling of streaming data and online error management. Furthermore, the chosen architecture considers the application's collaborative aspect, as well as online scanning, and user alerting. Aside from that, the built machine learning models are fine-tuned and examined on top of the acquired data to achieve the highest classification performance. Although the current app version is specially designed for researchers, it will soon be upgraded for general use to make end-users able to exchange valuable tags and information. In addition, the next version will be improved by online-deployed machine learning models to ensure real-time alerts about several road surface conditions. Those conditions will be further integrated when optimizing route plans and trips. The rest of this paper is structured as follows: Section 2 describes the related work in literature. Section 3 exposes our methodology for the application development. Application architecture is covered in Section 4. Section 4 also details how to use the application and the possible scenarios. The collected data, machine learning models, and evaluation metrics are described in Section 5.