Opportunistic sensing for road pavement monitoring

Road surface state monitoring is of main concern for road infrastructure owners. Hence dedicated measurement campaigns using laser scanning and image analysis are performed on a regular basis. Yet, this type of monitoring comes at a high labor cost and thus it is often limited in coverage and update frequency. This paper proposes opportunistic sensing as an alternative approach. Using sound and vibration sensing in cars that are on the road for other purposes and exploiting the advent of cheap communication, big data, and machine learning, timely information on road state is obtained. Results are compared to laser scanning for spatial frequencies between 0.1 and 100 cycles/m showing the applicability of the method. Results are also used for classification and labeling of road surfaces regarding their effect on rolling noise. Mapping illustrates the coverage of highways and local roads obtained in a few months with as few as seven cars

Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers

open access articleAutonomous robots that operate in the field can enhance their security and efficiency by accurate terrain classification, which can be realized by means of robot-terrain interaction-generated vibration signals. In this paper, we explore the vibration-based terrain classification (VTC), in particular for a wheeled robot with shock absorbers. Because the vibration sensors are usually mounted on the main body of the robot, the vibration signals are dampened significantly, which results in the vibration signals collected on different terrains being more difficult to discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade. The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of the existing feature-engineering and feature-learning classification methods; and (2) According to the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM (1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods, which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project; meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method (LSTM) by 8.23%

Utilizing artificial intelligence and machine learning for monitoring and modeling road conditions

Abstract. Road maintenance requires resources increasingly as climate change and high traffc volume in populous areas infict a signifcant strain on the traffc infrastructure. In rural areas, the car is usually the only mode of transport and long driving distances with high average speeds are covered on a daily basis. The majority of maintenance resources are located in densely populated cities, making the maintenance of rural roads challenging and expensive. New scalable methods to optimize the usage of road maintenance resources are demanded. This thesis reviews several artifcial intelligence and machine learning based techniques and systems designed for monitoring, evaluating, and predicting road condition and deterioration. In the implementation part of the thesis, two classifcation models, based on logistic regression and support vector machines, are trained to classify fve different types of normal or damaged road segments from vertical acceleration data measured with smartphone sensors. A classifcation accuracy of 70.9% was achieved with logistic regression and 73.9% with support vector machine. The results of the implementation provide more evidence that vibration-based road condition monitoring systems can identify road anomalies with good accuracy and could have practical utility in road maintenance related tasks.Tekoälyn ja koneoppimisen hyödyntäminen tien kunnon tunnistamisessa ja mallintamisessa. Tiivistelmä. Teiden huoltotoimenpiteet vaativat resursseja enenevissä määrin, sillä ilmastonmuutos ja vilkastuva liikenne tiheästi asutuilla alueilla kuormittavat liikenneinfrastruktuuria merkittävästi. Harvaan asutuilla alueilla auto on usein ainoa kulkuväline, ja asukkaat keskimäärin ajavat pidempiä matkoja suuremmalla keskinopeudella. Suurin osa teiden huoltoon vaadittavista resursseista keskittyy tiheään asutuille taajama-alueille, tehden harvaan asuttujen alueiden tiestön huollosta haastavaa. Uusia skaalautuvia menetelmiä teiden huoltoon vaadittavien resurssien optimoimiseksi tarvitaan. Tässä tutkielmassa tarkastellaan erilaisia tekoälyyn ja koneoppimiseen pohjautuvia menetelmiä ja järjestelmiä teiden kunnon tarkastamista, arviointia ja mallintamista varten. Tutkielman suoritusosassa kaksi luokittelumallia, jotka pohjautuvat logistiseen regressioon ja tukivektorikoneeseen, koulutetaan erottamaan viisi erityyppistä normaalia tai vaurioitunutta tieosuutta älypuhelimen liikesensoreilla kerätyistä vertikaalisista kiihtyvyysanturimittauksista. Logistinen regressiomalli luokitteli testidataa keskimäärin 70.9% tarkkuudella, kun taas tukivektorikoneeseen perustuva malli saavutti vastaavasti 73.9% luokittelutarkkuuden. Suoritusosan tulokset antavat näyttöä siitä, että värähtelymittauksiin perustuvat tien kunnon tunnistamiseen suunnitellut järjestelmät voivat tunnistaa erinäisiä poikkeamia tien pinnassa hyvällä tarkkuudella, ja että näistä järjestelmistä voisi olla hyötyä teiden huoltoon liittyvissä toimenpiteissä

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Characterization of Road Condition with Data Mining Based on Measured Kinematic Vehicle Parameters

This work aims at classifying the road condition with data mining methods using simple acceleration sensors and gyroscopes installed in vehicles. Two classifiers are developed with a support vector machine (SVM) to distinguish between different types of road surfaces, such as asphalt and concrete, and obstacles, such as potholes or railway crossings. From the sensor signals, frequency-based features are extracted, evaluated automatically with MANOVA. The selected features and their meaning to predict the classes are discussed. The best features are used for designing the classifiers. Finally, the methods, which are developed and applied in this work, are implemented in a Matlab toolbox with a graphical user interface. The toolbox visualizes the classification results on maps, thus enabling manual verification of the results. The accuracy of the cross-validation of classifying obstacles yields 81.0% on average and of classifying road material 96.1% on average. The results are discussed on a comprehensive exemplary data set

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality--vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces