Live Road Condition Assessment with Internal Vehicle Sensors

2021 ◽  
pp. 036119812110168
Author(s):  
Eyal Levenberg ◽  
Asmus Skar ◽  
Shahrzad M. Pour ◽  
Ekkart Kindler ◽  
Matteo Pettinari ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Relevant Information ◽  
Pavement Management ◽  
Sensor Data ◽  
Electric Cars ◽  
Condition Evaluation ◽  
Pavement Management Systems ◽  
Road Condition ◽  
Spatio Temporal ◽  
Vehicle Sensors

Modern cars are equipped with many sensors that measure information about the vehicle and its surroundings. These measurements are therefore related to the ride-surface conditions over which the vehicle is passing. The paper commences by outlining a four-component vision for performing road condition evaluation based on in-vehicle sensor readings and subsequent feeding of pavement management systems (PMSs) with live condition information. This is expected to enrich the functionalities of PMSs, and ultimately lead to improved maintenance and repair decisions. Next the LiRA (Live Road Assessment) project—an ongoing proof-of-concept attempt to realize the vision components—is presented. The project elements and software architecture are described in detail, listing any assumptions, compromises, and challenges. LiRA involves a fleet of 400 electric cars operating in Copenhagen, both within the city streets and nearby highways. Sensor data collection is performed with a customized Internet of Things (IoT) device installed in the cars. Data processing and structuring involve new software tools for quality control, spatio-temporal interpolation, and map matching. A hybrid approach, combining machine learning models with physical (mechanics-based) models, is currently being applied to convert data into relevant information for PMSs. Based on the experience thus far with LiRA, the vision actualization is deemed achievable, workable, and up-scalable.

Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management

2020 ◽  
Vol 2674 (7) ◽  
pp. 585-595
Author(s):  
Moksheeth Padarthy ◽  
Mohammed Sami ◽  
Emiliano Heyns
Keyword(s):  
Asset Management ◽  
Real Life ◽  
Pavement Management ◽  
Machine Learning Techniques ◽  
Lateral Acceleration ◽  
The Road ◽  
Pavement Management Systems ◽  
Learning Techniques ◽  
The Right ◽  
Vehicle Sensors

One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios.

scholarly journals Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 579
Author(s):  
Margarida Amândio ◽  
Manuel Parente ◽  
José Neves ◽  
Paulo Fonseca
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Pavement Management ◽  
Condition Evaluation ◽  
Pavement Management Systems ◽  
Pavement Monitoring ◽  
Long Time ◽  
Future Direction ◽  
Smart Technologies

Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

Design of database systems for optimized spatio-temporal querying to facilitate monitoring, analysing and forecasting in the "Internet of Water"

2020 ◽  
Author(s):  
Erik Bollen ◽  
Brianna R. Pagán ◽  
Bart Kuijpers ◽  
Stijn Van Hoey ◽  
Nele Desmet ◽  
...  
Keyword(s):  
Real Time ◽  
Hybrid Approach ◽  
Water System ◽  
Database Systems ◽  
Data Representation ◽  
Water Systems ◽  
Flow Path ◽  
Graph Representation ◽  
Sensor Data ◽  
Spatio Temporal

<p>Monitoring, analysing and forecasting water-systems, such as rivers, lakes and seas, is an essential part of the tasks for an environmental agency or government. In the region of Flanders, in Belgium, different organisations have united to create the ”Internet of Water” (IoW). During this project, 2500 wireless water-quality sensors will be deployed in rivers, canals and lakes all over Flanders. This network of sensors will support a more accurate management of water systems by feeding real-time data. Applications include monitoring real-time water-flows, automated warnings and notifications to appropriate organisations, tracing pollution and the prediction of salinisation.</p><p>Despite the diversity of these applications, they mostly rely on a correct spatial representation and fast querying of the flow path: where does water flow to, where can the water come from, and when does the water pass at certain locations? In the specific case of Flanders, the human-influenced landscape provides additional complexity with rivers, channels, barriers and even cycles. Numerous models and systems exist that are able to answer the above questions, even very precisely, but they often lack the ability to produce the results quickly enough for real-time applicability that is required in the IoW. Moreover, the rigid data representation makes it impossible to integrate new data sources and data types, especially in the IoW, where the data originates from vastly different backgrounds.</p><p>In this research, we focus on the performance of spatio-temporal queries taking into account the spatial configuration of a strongly human-influenced water system and the real-time acquisition and processing of sensor data. The use of graph-database systems is compared with relational-database systems to store topologies and execute recursive path-tracing queries. Not only storing and querying are taken into account, but also the creation and updating of the topologies are an essential part. Moreover, the advantages of a hybrid approach that integrates the graph-based databases for spatial topologies with relational databases for temporal and water-system attributes are investigated. The fast querying of both upstream and downstream flow-path information is of great use in various applications (e.g., pollution tracking, alerting, relating sensor signals, …). By adding a wrapper library and creating a standardised result graph representation, the complexity is abstracted away from the individual applications.</p>

Evaluation of Network-Level Data Collection Variability and its Influence on Pavement Evaluation Utilizing Random Forest Method

2021 ◽  
pp. 036119812098043
Author(s):  
Xiaoyang Jia ◽  
Mark Woods ◽  
Hongren Gong ◽  
Di Zhu ◽  
Wei Hu ◽  
...  
Keyword(s):  
Data Collection ◽  
High Variability ◽  
Pavement Management ◽  
Data Sets ◽  
State Highway ◽  
Condition Evaluation ◽  
Level Data ◽  
Pavement Evaluation ◽  
Data Variability ◽  
Parallel Tests

The use of pavement condition data to support maintenance and resurfacing strategies and justify budget needs becomes more crucial as more data-driven approaches are being used by the state highway agencies (SHAs). Therefore, it is important to understand and thus evaluate the influence of data variability on pavement management activities. However, owing to a huge amount of data collected annually, it is a challenge for SHAs to evaluate the influence of data collection variability on network-level pavement evaluation. In this paper, network-level parallel tests were employed to evaluate data collection variability. Based on the data sets from the parallel tests, classification models were constructed to identify the segments that were subject to inconsistent rating resulting from data collection variability. These models were then used to evaluate the influence of data variability on pavement evaluation. The results indicated that the variability of longitudinal cracks was influenced by longitudinal lane joints, lateral wandering, and lane measurement zones. The influence of data variability on condition evaluation for state routes was more significant than that for interstates. However, high variability of individual metrics may not necessarily lead to high variability of combined metrics.

scholarly journals A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest

2020 ◽  
Author(s):  
Ahlam Mallak ◽  
Madjid Fathi
Keyword(s):  
Random Forest ◽  
Random Search ◽  
Hybrid Approach ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Sensor Data ◽  
Sensor Systems ◽  
Hydraulic Test ◽  
Model Based ◽  
Detection And Diagnosis

In this work, A hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, spotting the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using 3-fold cross-validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. Followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data, in order to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, as well as the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time series multivariate sensor readings.

scholarly journals Sensing as a Service for the Internet of Things

2016 ◽  
Vol 4 (3) ◽  
pp. 28
Author(s):  
Omar Subhi Aldabbas
Keyword(s):  
Internet Of Things ◽  
Cost Effective ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Heterogeneous Services ◽  
Coordination Languages ◽  
Spatio Temporal ◽  
Temporal Events ◽  
New Applications ◽  
Common Application

Internet of Things (IoT) is a ubiquitous embedded ecosystem known for its capability to perform common application functions through coordinating resources, which are distributed on-object or on-network domains. As new applications evolve, the challenge is in the analysis and usage of multimodal data streamed by diverse kinds of sensors. This paper presents a new service-centric approach for data collection and retrieval. This approach considers objects as highly decentralized, composite and cost effective services. Such services can be constructed from objects located within close geographical proximity to retrieve spatio-temporal events from the gathered sensor data. To achieve this, we advocate Coordination languages and models to fuse multimodal, heterogeneous services through interfacing with every service to achieve the network objective according to the data they gather and analyze. In this paper we give an application scenario that illustrates the implementation of the coordination models to provision successful collaboration among IoT objects to retrieve information. The proposed solution reduced the communication delay before service composition by up to 43% and improved the target detection accuracy by up to 70%, while maintaining energy consumption 20% lower than its best rivals in the literature.

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