Road surface type classification based on inertial sensors and machine learning

Menegazzo, Jeferson; Wangenheim, Aldo von

doi:10.1007/s00607-021-00914-0

Cited by 24 publications

(9 citation statements)

References 45 publications

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“…In Ref. 29 , the authors collected nine datasets named Passive Vehicular Sensors Dataset (PVS 1–9) using Raspberry Pi and MPU-9250 modules, external GPS, and a camera. They recorded various measurements, including acceleration, gyroscope, magnetometer, temperature, location, and speed data, using two MPU-9250 modules, which were distributed in the vehicle.…”

Section: Resultsmentioning

confidence: 99%

“…We compared the performance of our proposed model against 29 best-performing model to evaluate its effectiveness. The results showed that our model achieved a slightly higher overall accuracy (94.78%), outperforming their best-performing model (91.44%), which indicates the effectiveness of our approach to accurately classifying multivariate time series data as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Evaluation of data representation techniques for vibration based road surface condition classification

Raslan,

Alrahmawy,

Mohammed

et al. 2024

Sci Rep

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The accurate classification of road surface conditions plays a vital role in ensuring road safety and effective maintenance. Vibration-based techniques have shown promise in this domain, leveraging the unique vibration signatures generated by vehicles to identify different road conditions. In this study, we focus on utilizing vehicle-mounted vibration sensors to collect road surface vibrations and comparing various data representation techniques for classifying road surface conditions into four classes: normal road surface, potholes, bad road surface, and speedbumps. Our experimental results reveal that the combination of multiple data representation techniques results in higher performance, with an average accuracy of 93.4%. This suggests that the integration of deep neural networks and signal processing techniques can produce a high-level representation better suited for challenging multivariate time series classification issues.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Evaluation of data representation techniques for vibration based road surface condition classification

Raslan,

Alrahmawy,

Mohammed

et al. 2024

Sci Rep

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“…Using a laser or color camera mounted at the back of the vehicles, a top view of the road, as used by [43][44][45][46][47], focuses on crack detections and potholes. A wide-angle view of the road, using a camera mounted on the front of the dashboard or top of the car, as used by [32,48,49], is used for detecting types of cracks, potholes, and types of surfaces and surface ratings.…”

Section: Data Acquisition Processmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

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“…When a car passes a surface of different types and conditions, changes in the speed, trajectory, and smoothness of the vehicle's movement occur. Thanks to the use of motion sensors, and sensors of the internal combustion engine, it is possible to classify the type of road surface, driving style, and road traffic [1]. Target variable classification [2] based on data gathered during the vehicle movement allows to combine all of this into effective approach for fast classification to determine the condition of the road surface at a certain moment of the time of operation of the sensors and additionally geolocation of the vehicle at the same moment.…”

Section: Introductionmentioning

confidence: 99%

A New Information System for Road Surface Condition Classification Using Machine Learning Methods and Parallel Calculation

Mochurad¹,

Ilkiv²,

Kravchenko³

2023

CSIT

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Modern information systems are increasingly used in various areas of our life. One of these is the quality control of the condition of the road surface in order to carry out repair work on time if necessary. The machine learning method can facilitate the control process, which was demonstrated in this work. Analyzing the road surface condition using image classification requires much pre-classified data and decent computing power. As the modern need for proper quality control of the road surface is high, it is possible to analyze using sensor-recorded data in tabular form and machine learning methods, which should show high accuracy of the classification results. Development and research of an information system for classifying the condition of the road surface were described in this paper, including ways for optimizing similar approaches and improving the results obtained through the use of a greater number of features, in particular, taking into account not only the speed indicators at the given time of the car's movement but also the performance indicators of internal combustion engine. As a result, an information system was developed that classifies the road surface condition using features obtained from various types of sensors and recorded in tabular form. Machine learning methods such as Random Forest, Decision Tree, Support Vector Method, and AutoML library were used to compare accuracy results using a large set of artificial intelligence methods. The best results were obtained using the Random Forest ensemble machine learning method. The analysis of the classifier according to various parameters was carried out, and a search for the best hyperparameters was performed. At the same time, achieving a 91.9% accuracy of road surface condition classification was possible. Parallel calculations were used during model training. As a result, training time was decreased by 5 times with the use of the CPU and by 51 times with the help of the GPU.

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Road surface type classification based on inertial sensors and machine learning

Cited by 24 publications

References 45 publications

Evaluation of data representation techniques for vibration based road surface condition classification

Evaluation of data representation techniques for vibration based road surface condition classification

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

A New Information System for Road Surface Condition Classification Using Machine Learning Methods and Parallel Calculation

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