MagTrack: Detecting Road Surface Condition using Smartphone Sensors and Machine Learning

2019 ◽  
Author(s):  
Meenu Rani Dey ◽  
Utkalika Satapathy ◽  
Pranali Bhanse ◽  
Bhabendu Kr. Mohanta ◽  
Debasish Jena
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Road Surface ◽  
Smartphone Sensors

scholarly journals Road Surface Monitoring Using Smartphone Sensors: A Review

2021 ◽  
Author(s):  
Shahram Sattar ◽  
Songnian Li ◽  
Michael A. Chapman
Keyword(s):  
Anomaly Detection ◽  
Surface Condition ◽  
Low Frequency ◽  
Road Surface ◽  
Detection Accuracy ◽  
Road Maintenance ◽  
Key Factor ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

scholarly journals A Machine Learning Approach to Road Surface Anomaly Assessment Using Smartphone Sensors

2020 ◽  
Vol 20 (5) ◽  
pp. 2635-2647 ◽  
Author(s):  
Akanksh Basavaraju ◽  
Jing Du ◽  
Fujie Zhou ◽  
Jim Ji
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Smartphone Sensors

scholarly journals Research on Road Adhesion Condition Identification Based on an Improved ALexNet Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
QiMing Wang ◽  
JinMing Xu ◽  
Tao Sun ◽  
ZhiChao Lv ◽  
GaoQiang Zong
Keyword(s):  
Machine Learning ◽  
Surface Condition ◽  
Image Features ◽  
Road Surface ◽  
Driving Safety ◽  
Error Performance ◽  
Training Time ◽  
The Road ◽  
Network Training ◽  
Proposed Model

Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.

scholarly journals A Crowdsourcing Technique For Road Surface Monitoring Using Smarthphone Sensors

2021 ◽  
Author(s):  
Shahram Sattar
Keyword(s):  
Information System ◽  
Surface Condition ◽  
Road Surface ◽  
Detection Accuracy ◽  
Active Monitoring ◽  
The Road ◽  
Detection Approach ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor in providing safe road infrastructure for road users. As a result, road surface condition monitoring aims to detect road surface anomalies such potholes, cracks and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road rehabilitation and maintenance. Several studies have been developed to utilize smartphone sensors (e.g., Global Positioning system (GPS) and accelerometers) mounted on a moving vehicle to collect and process the data to monitor and tag roadway surface defects. Geotagged images or videos from the roadways have also been used to detect the road surface anomalies. However, existing studies are limited to identifying roadway anomalies mainly from a single source or lack the utility of combined and integrated multi-sensors in terms of accuracy and functionality. Therefore, low-cost, more efficient pavement evaluation technologies and a centralized information system are necessary to provide the most up-to-date information about the road status due to the dynamic changes on the road surface This information will assist transportation authorities to monitor and enhance the road surface condition. In this research, a probabilistic-based crowdsourcing technique is developed to detect road surface anomalies from smartphone sensors such as linear accelerometers, gyroscopes and GPS to integrate multiple detections accurately. All case studies from the proposed detection approach showed an approximate 80% detection accuracy (from a single survey) which supports the inclusiveness of the detection approach. In addition, the results of the proposed probabilistic-based integration approach indicated that the detection accuracy can be further improved by 5 to 20% with multiple detections conducted by the same vehicle along the same road segments. Finally, the development of the web-based Geographic Information System (GIS) platform would facilitate the real-time and active monitoring of road surface anomalies and offer further improvement of road surface quality control in large cities like Toronto.

scholarly journals Smartphone Sensing of Road Surface Condition and Defect Detection

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5433
Author(s):  
Dapeng Dong ◽  
Zili Li
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
Large Scale ◽  
Learning Algorithm ◽  
Surface Condition ◽  
Large Population ◽  
Road Surface ◽  
Maintenance Cost ◽  
Accelerometer Data ◽  
Road Maintenance

Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.

scholarly journals Road Surface Monitoring Using Smartphone Sensors: A Review

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3845 ◽  
Author(s):  
Shahram Sattar ◽  
Songnian Li ◽  
Michael Chapman
Keyword(s):  
Anomaly Detection ◽  
Surface Condition ◽  
Low Frequency ◽  
Road Surface ◽  
Detection Accuracy ◽  
Road Maintenance ◽  
Key Factor ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

scholarly journals Estimating Spatiotemporal Information from Behavioral Sensing Data of Wheelchair Users by Machine Learning Technologies

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 114 ◽  
Author(s):  
Ikuko Yairi ◽  
Hiroki Takahashi ◽  
Takumi Watanabe ◽  
Kouya Nagamine ◽  
Yusuke Fukushima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Surface Condition ◽  
Learning Technologies ◽  
Road Surface ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Prototype System ◽  
Wheelchair Users ◽  
The Road

Recent expansion of intelligent gadgets, such as smartphones and smart watches, familiarizes humans with sensing their activities. We have been developing a road accessibility evaluation system inspired by human sensing technologies. This paper introduces our methodology to estimate road accessibility from the three-axis acceleration data obtained by a smart phone attached on a wheelchair seat, such as environmental factors, e.g., curbs and gaps, which directly influence wheelchair bodies, and human factors, e.g., wheelchair users’ feelings of tiredness and strain. Our goal is to realize a system that provides the road accessibility visualization services to users by online/offline pattern matching using impersonal models, while gradually learning to improve service accuracy using new data provided by users. As the first step, this paper evaluates features acquired by the DCNN (deep convolutional neural network), which learns the state of the road surface from the data in supervised machine learning techniques. The evaluated results show that the features can capture the difference of the road surface condition in more detail than the label attached by us and are effective as the means for quantitatively expressing the road surface condition. This paper developed and evaluated a prototype system that estimated types of ground surfaces focusing on knowledge extraction and visualization.

scholarly journals Road Surface Monitoring Using Smartphone Sensors: A Review

2021 ◽  
Author(s):  
Shahram Sattar ◽  
Songnian Li ◽  
Michael A. Chapman
Keyword(s):  
Anomaly Detection ◽  
Surface Condition ◽  
Low Frequency ◽  
Road Surface ◽  
Detection Accuracy ◽  
Road Maintenance ◽  
Key Factor ◽  
Smartphone Sensors ◽  
Road Surface Monitoring ◽  
Surface Monitoring

Road surface monitoring is a key factor to providing smooth and safe road infrastructure to road users. The key to road surface condition monitoring is to detect road surface anomalies, such as potholes, cracks, and bumps, which affect driving comfort and on-road safety. Road surface anomaly detection is a widely studied problem. Recently, smartphone-based sensing has become increasingly popular with the increased amount of available embedded smartphone sensors. Using smartphones to detect road surface anomalies could change the way government agencies monitor and plan for road maintenance. However, current smartphone sensors operate at a low frequency, and undersampled sensor signals cause low detection accuracy. In this study, current approaches for using smartphones for road surface anomaly detection are reviewed and compared. In addition, further opportunities for research using smartphones in road surface anomaly detection are highlighted.

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