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%.

Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework

“…Zang et al [17] mounted the smartphone on a bicycle, collected road surface information based on the recorded acceleration changes, and derived the road surface changes using the threshold value method. In addition to the location information from smartphone sensors, these methods often require data from supplementary sensors like cameras, accelerometers, gyroscopes, and audio sensors [9,14,26]. Li et al [16] proposed a road surface detection method based on the continuous wavelet transform (CWT).…”

“…(3) Apply a similar process of second and higher scattering layers to the output of the first layer but after the modulus operation, and obtain the result U2[j, k]x(t), which allows the capture of more complex and advanced features of the data, as denoted by Equation (8). Then, repeat the low-pass filtering again, and obtain the result S2x(t), as denoted by Equation (9).…”

“…Therefore, sensing the surface condition to gather information like BFRSs is essential for urban transportation [8,9]. Traditional methods of road surface sensing rely on professional equipment and personnel, such as LiDAR, photogrammetry, and remote sensing [8,[10][11][12][13].…”

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

“…The classical machine learning cycle is shown in Figure 5. Machine learning techniques have been increasingly applied in automotive system security to detect and mitigate security threats in real-time [234][235][236][237]. Machine learning algorithms can analyze vast amounts of data collected from various sensors, network traffic, and system logs to identify anomalous behavior and potential security breaches [238][239][240][241].…”