Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles

Mesgarpour, Mohammad; Landa-Silva, Dario; Dickinson, Ian

doi:10.1007/978-3-642-41647-7_16

Cited by 26 publications

(23 citation statements)

References 18 publications

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“…Logistics complexity coupled with large transport networks has required the widespread use of sensors, tracking devices, and mobile communication equipment in order to enable such developments [2]. These devices constantly gather information of vehicles and their journeys.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Incident Hot Spots Identification: An Approach for Big Data

Triguero

Figueredo

Mesgarpour

et al. 2017

2017 IEEE Trustcom/BigDataSE/Icess

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract-In this work we introduce a fast big data approach for road incident hot spot identification using Apache Spark. We implement an existing immuno-inspired mechanism, namely SeleSup, as a series of MapReduce-like operations. SeleSup is composed of a number of iterations that remove data redundancies and result in the detection of areas of high likelihood of vehicles incidents. It has been successfully applied to large datasets, however, as the size of the data increases to millions of instances, its performance drops significantly. Our objective therefore is to re-conceptualise the method for big data. In this paper we present the new implementation, the challenges faced when converting the method for the Apache Spark platform as well as the outcomes obtained. For our experiments we employ a large dataset containing hundreds of thousands of Heavy Good Vehicles incidents, collected via telematics. Results show a significant improvement in performance with no detriment to the accuracy of the method.

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Section: Introductionmentioning

confidence: 99%

Vehicle Incident Hot Spots Identification: An Approach for Big Data

Triguero

Figueredo

Mesgarpour

et al. 2017

2017 IEEE Trustcom/BigDataSE/Icess

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“…These algorithms, and others discussed in public patent filings, are fed information from the conventional On-Board Diagnostic network, and occasionally incorporate proprietary sensor information beyond that included with standard diagnostic parameters, such as oil pressure. Cloud-based tools such as OnStar have enabled real time access to OEM diagnostic data and heuristics [5]. However, such algorithms duplicate in situ results or are open-loop, inferring oil life based on historic data and rarely or never validating accuracy.…”

Section: Introductionmentioning

confidence: 98%

Vehicular engine oil service life characterization using On-Board Diagnostic (OBD) sensor data

Siegel

Bhattacharyya

Deshpande

2014

IEEE SENSORS 2014 Proceedings

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Standardized vehicular On-Board Diagnostics (OBD) systems offer access to information commonly used for fault notification and reactive diagnostic services. Recently, there have been efforts to use OBD data to diagnose and predict faults prior to catastrophic failure events. Engine oil service life, a parameter directly related to engine longevity, is difficult to measure conventionally. We show that the rate of engine coolant temperature rise, readily obtainable through the OBD suite, can serve as a proxy to indicate the remaining engine oil life. We demonstrate consistent results for one vehicle under similar environmental and unloaded engine operating conditions. We also examine the validity of this approach under varying environmental and engine loading conditions with tests on a second vehicle.

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“…Advances in ubiquitous computing, wireless communication and the widespread use of sensors in transportation has allowed for advances in vehicle health monitoring [1], [2], general transport maintenance [3]- [7] and incident prevention conditioned to data gathered regarding human behaviour [8]- [10]. Research in driving behaviour, vehicle monitoring, driving style prediction and driver modelling has therefore been increasing significantly over the last decade, as there is high demand for intelligent solutions to improve driving economy and safety.…”

Section: Introductionmentioning

confidence: 99%

Towards Real-Time Heavy Goods Vehicle Driving Behaviour Classification in the United Kingdom

Agrawal

Mase

Figueredo

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Determining the driving styles and the factors causing incidents in real time could assist stakeholders to promote actions and develop feedback systems to reduce risks, costs and to increase safety in roads. This paper presents a classification system for Heavy Goods Vehicles (HGVs) drivers, using a core set of driving pattern stereotypes which were uncovered from driving incidents across three years i.e. 2014, 2015 and 2016. To achieve that, the driving stereotypes are established by employing a 2-stage ensemble classification framework followed by a profile labelling algorithm to define the set of driving stereotypes. Very similar stereotypes are later merged to form the core driving stereotypes for UK HGV drivers. Upon establishing core driving stereotypes across these three years, a decision tree classifier learns the classification rules to identify the driving stereotypes for the HGV drivers in a new dataset. High accuracy is achieved, indicating that the core driving patterns uncovered in this work could potentially be employed to identify UK HGV driving patterns in real-time.

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Overview of Telematics-Based Prognostics and Health Management Systems for Commercial Vehicles

Cited by 26 publications

References 18 publications

Vehicle Incident Hot Spots Identification: An Approach for Big Data

Vehicle Incident Hot Spots Identification: An Approach for Big Data

Vehicular engine oil service life characterization using On-Board Diagnostic (OBD) sensor data

Towards Real-Time Heavy Goods Vehicle Driving Behaviour Classification in the United Kingdom

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