scholarly journals An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5564
Author(s):  
Chao Wu ◽  
Zhen Wang ◽  
Simon Hu ◽  
Julien Lepine ◽  
Xiaoxiang Na ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Classification Performance ◽  
Sensor Data ◽  
The Road ◽  
Frequency Domains ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Instrumented Vehicles ◽  
Pothole Detection

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.

scholarly journals Development of Platooning Technology with Road Condition Monitoring

2021 ◽  
Vol 9 (10) ◽  
pp. 515-518
Author(s):  
Srinivaas A
Keyword(s):  
Time Delay ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Sensor Data ◽  
Automated Driving ◽  
The Road ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Condition Monitoring System ◽  
Lead Vehicle

Abstract: In this paper, we present a complete platooning system using a time-delay algorithm. The platooning is achieved by measuring the driver inputs from the lead vehicle and sending these inputs to the trail vehicle with a time-delay so that the trail vehicle can exactly mimic the motion of the lead vehicle. This system also does a road condition monitor as an add-on benefit which will help in assisting the driver of the trail vehicle/vehicle which takes the same path. The function of this monitoring system is to analyse the road surface using a lead vehicle and acquire sensor data, this acquired sensor data helps in assisting drivers who take the same track. The combination of both this platooning method and road condition monitoring system could potentially reduce the current risk of utilising this semi-automated driving system. Index terms: Platooning, Semi-automated driving, Road condition monitoring, Time-delay algorithm.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Monitoring Vehicle Communication and Road Condition in VANET

2020 ◽  
pp. 530-537
Author(s):  
Saravanakumar S
Keyword(s):  
Ad Hoc ◽  
Cloud Service ◽  
Security And Privacy ◽  
Geologic Hazard ◽  
The Road ◽  
Computing Services ◽  
Cloud Server ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Reported Data

The connected vehicular ad-hoc network (VANET) and cloud computing technology allows entities in VANET to enjoy the advantageous storage and computing services offered by some cloud service provider. However, the advantages do not come free since their combination brings many new security and privacy requirements for VANET applications. In this article, we investigate the cloud-based road condition monitoring (RCoM) scenario, where the authority needs to monitor real-time road conditions with the help of a cloud server so that it could make sound responses to emergency cases timely. When some bad road condition is detected, e.g., some geologic hazard or accident happens, vehicles on site are able to report such information to a cloud server engaged by the authority. We focus on addressing three key issues in RCoM. First, the vehicles have to be authorized by some roadside unit before generating a road condition report in the domain and uploading it to the cloud server. Second, to guarantee the privacy against the cloud server, the road condition information should be reported in ciphertext format, which requires that the cloud server should be able to distinguish the reported data from different vehicles in ciphertext format for the same place without compromising their confidentiality. Third, the cloud server and authority should be able to validate the report source, i.e., to check whether the road conditions are reported by legitimate vehicles. To address these issues, we present an efficient RCoM scheme, analyze its efficiency theoretically, and demonstrate the practicality through experiments

Smart pothole detection system using vehicle-mounted sensors and machine learning

2019 ◽  
Vol 9 (1) ◽  
pp. 91-102 ◽  
Author(s):  
Ali Anaissi ◽  
Nguyen Lu Dang Khoa ◽  
Thierry Rakotoarivelo ◽  
Mehrisadat Makki Alamdari ◽  
Yang Wang
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Pothole Detection

scholarly journals DeepBus : Machine learning based real time pothole detection system for smart transportation using IoT

2020 ◽  
Vol 3 (3) ◽  
Author(s):  
Kashish Bansal ◽  
Kashish Mittal ◽  
Gautam Ahuja ◽  
Ashima Singh ◽  
Sukhpal Singh Gill
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Detection System ◽  
Smart Transportation ◽  
Pothole Detection

scholarly journals Road Condition Monitoring with Grading System

2020 ◽  
pp. 64-69
Keyword(s):  
Young Adults ◽  
Condition Monitoring ◽  
Daily Activities ◽  
Grading System ◽  
The Road ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
Entire World ◽  
Two Wheeler

India has the highest number of two-wheeler riders in the entire world. As Indians think that twowheelers are more convenient a lot of people use them for their daily activities. Delivery boys for a lot of companies also prefer to use a two-wheeler as it is more economically convenient. Along with this, people also use twowheelers for rushing to the workplace avoiding a lot of traffic. The majority of these people can be classified as young adults. A lot of people complain about back issues due to the bad road conditions that they face while travelling every day. Our system uses the sensor consisting of the accelerometer and the gyroscope to analyze the condition of the road and classify how bad the current condition of the road actually is. The system will not only classify the road as good or bad but also provide a rating to the road based on how severe the condition of the road actually is. The sensors will be calibrated according to a particular vehicle which will be beneficial for the rider. The system will also provide the best option of the road from travelling from point A to point B provided if there are multiple options available and the analysis of all the options has been done previously.

scholarly journals Evaluation of Road Service Performance Based on Human Perception of Vibration While Driving Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Wenbo Shi ◽  
Ming Li ◽  
Jingxuan Guo ◽  
Kaixuan Zhai
Keyword(s):  
Human Perception ◽  
Service Performance ◽  
Road Maintenance ◽  
Vibration Level ◽  
Vibration Acceleration ◽  
The Road ◽  
Vibration Data ◽  
Road Condition ◽  
Road Condition Monitoring ◽  
On The Road

Road surface monitoring is a significant issue in providing smooth road infrastructure for vehicles, and the key to road condition monitoring is to detect road potholes that affect driving comfort and transportation safety. This paper presents a simple, efficient, and accurate way to evaluate road service performance based on the acquisition of road vibration data by vibration sensors installed in vehicles. Inspired by the discrete fast Fourier transform, the vibration acceleration is processed, and the RMS value of vibration acceleration at 1/2 octave is calculated, after which the road vibration level is calculated. The vibration level is optimized according to the human body’s sensitivity to different frequencies of vibration, resulting in road service performance indicators that can reflect the human body’s real feelings. According to the road service performance index values on the road grading, combined with GPS data on the electronic map color block labeling, the results obtained for the road condition warning, road maintenance, driver route selection have an important significance.

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