Intelligent road surface quality evaluation using rough mereology

Abstract

The road surface condition information is very useful for the safety of road users and to inform road administrators for conducting appropriate maintenance. Roughness features of road surface; such as speed bumps and potholes, have bad effects on road users and their vehicles. Usually speed bumps are used to slow motor-vehicle traffic in specific areas in order to increase safety conditions. On the other hand driving over speed bumps at high speeds could cause accidents or be the reason for spinal injury. Therefore informing road users of the position of speed bumps through their journey on the road especially at night or when lighting is poor would be a valuable feature. This paper exploits a mobile sensor computing framework to monitor and assess road surface conditions. The framework measures the changes in the gravity orientation through a gyroscope and the shifts in the accelerometer's indications, both as an assessment for the existence of speed bumps. The proposed classification approach used the theory of rough mereology to rank the modified data in order to make a useful recommendation to road users.

Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

“…Khaleghian and Taheri [34] applied fuzzy logic to recognize the surface type in grass, soil, concrete or asphalt. Fouad et al [21] used rough mereology theory to identify speed bumps. Allouch et al [3] used decision trees, support vector machines (SVM) and Naive Bayes, and Singh et al [63] used a SVM, both to identify generic road conditions.…”

“…High-pass Fast Fourier Transform-FFT N/A N/A [18] High-pass Fast Fourier Transform-FFT N/A N/A [20] Wavelet N/A N/A [21] N/A N/A N/A [22] N/A N/A N/A [23] High pass N/A N/A [25] Wavelet transform with morlet wavelet N/A N/A [26] High pass N/A N/A [28] Low-pass second-order Butterworth standard deviation N/A N/A [29] N/A N/A N/A [30] N/A N/A N/A [31] Root mean square (RMS)…”

Pavement Management Utilizing Mobile Crowd Sensing

“…It is worth mentioning that the Kalman Filter (KF) was utilized over ten times in the past five years. Fouad et al [87] used KF to reduce noisy samples of GPS readings. Tan et al [88] regard KF as a general denoising method and utilized KF to remove the noise from the accelerometer and gyroscope.…”

Street SAFE - Road Fault Monitoring and Reporting

“…A crowdsourcing road surface monitoring system called CRSM was developed that was able to detect road potholes and rate the road roughness levels through a hardware module installed on distributed vehicles and wirelessly connected to a central server [3]. The CRSM module was consisted of a microcontroller (MCU), a GSM module, a GPS device and an accelerometer sensor to identify road vibration and obtain location and vehicle velocity during the vehicle travel.…”

“…The CRSM module was consisted of a microcontroller (MCU), a GSM module, a GPS device and an accelerometer sensor to identify road vibration and obtain location and vehicle velocity during the vehicle travel. CRSM provided a road roughness classification algorithm to determine the road roughness level and an improved Gaussian Mixture Model so that the event detection threshold can be changed to a parameter which is roughly linear with the current velocity together with learning rate updated based on a high learning rate which is used for big velocity changes as opposed to a small learning rate for small velocity changes [3].…”