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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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    <item>
      <title>Extending life of vehicles within electromobility era (EVE)</title>
      <link>https://trid.trb.org/View/2491174</link>
      <description><![CDATA[The EVE project has been a research project to explore the use of data, analytics, and machine learning to prolong the lifetime of electric vehicles. In this endeavor, the project has focused on the most crucial components of an electric drivetrain, such as the battery, ECUs, charging hardware, and charging infrastructure to identify potentials to extend the lifetime of the components. Extending the lifetime of these vital components will in turn have a large impact on the total cost and environmental impact of electric vehicles, as the drivetrain and energy storage systems stand for a significant amount of the cost and environmental footprint of the heavy-duty vehicles. During the project, we have investigated different techniques and methods. For example, Transfer Learning methods were utilized to transfer insights from the older hybrid buses into newer generations, providing a significant increase in the ability to calculate and model Battery State of Health over classical Supervised Regression Models. The project has also utilized Machine Learning methods to create predictive maintenance algorithms for the drivetrain, enabling faster identification of errors and, therefore, a longer lifetime of the vehicles. The project has also used FLAML to identify and train models on real-world data to predict the energy consumption of full-electric vehicles in different driving scenarios, giving insights into critical components and drivers of consumption in the vehicles.]]></description>
      <pubDate>Fri, 17 Jan 2025 15:15:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2491174</guid>
    </item>
    <item>
      <title>Überprüfung der NOx-Emissionen in der Periodisch Technischen Inspektion (PTI)</title>
      <link>https://trid.trb.org/View/2401716</link>
      <description><![CDATA[Eine Überprüfung der NOx-Emissionen bei Kraftfahrzeugen (Kfz) ist aktuell in der Abgasuntersuchung (AU) noch nicht vorgesehen. Im Forschungsprojekt 84.536/2021 (siehe 01838821) der BASt wurde ein Ansatz ermittelt, der die Überprüfung der NOx-Emissionen im Rahmen der AU mithilfe von im Fahrzeug integrierten Sensoren ermöglichen könnte. Der Ansatz wurde umfangreich an einem Fahrzeug getestet und es wurde ein Schwellenwert für das untersuchte Fahrzeug ermittelt. Ziel des aktuellen Projekts ist es, den Ansatz an mehreren Fahrzeugen und Antriebskonzepten zu prüfen. Zusätzlich sollen erforderliche Datenfilter benannt werden, welche Messwerte von Fahrsituationen, die nicht für die Beurteilung der NOx-Emissionen herangezogen werden können, entfernen. Des Weiteren ist der bislang an einem Fahrzeug ermittelte Schwellenwert mit den ermittelten Schwellenwerten der durchzuführenden Studie zu vergleichen und zu klären, ob dieser fahrzeugspezifisch ist. Falls möglich, ist der Schwellenwert für Gruppen von Kfz zu klassifizieren. Zuletzt ist zu analysieren und zu bewerten, inwieweit die Methodik bei Fahrzeugen der EURO-Klasse 6 und gegebenenfalls 7 anwendbar ist und eine Empfehlung zu geben, ob und wie diese Methodik in die Periodisch Technische Inspektion (PTI) eingeführt werden könnte. ABSTRACT IN ENGLISH: NOx emissions from motor vehicles are not currently tested as part of the exhaust emissions test (AU). In the BASt research project 84.536/2021 (see 01838821), an approach was identified that could enable NOx emissions to be checked as part of the exhaust emissions test using sensors integrated in the vehicle. The approach was extensively tested on a vehicle and a threshold value was determined for the analysed vehicle. The aim of the current project is to test the approach on several vehicles and drive concepts. In addition, necessary data filters are to be identified which remove measured values from driving situations that cannot be used to assess NOx emissions. Furthermore, the threshold value determined so far on one vehicle should be compared with the threshold values determined in the current study and it should be clarified whether this is vehicle-specific. If possible, the threshold value for groups of motor vehicles should be classified. Finally, the extent to which the methodology can be applied to vehicles in EURO class 6 and possibly 7 should be analysed and evaluated and a recommendation should be made as to whether and how this methodology could be introduced into the Periodic Technical Inspection (PTI).]]></description>
      <pubDate>Tue, 09 Jul 2024 10:38:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2401716</guid>
    </item>
    <item>
      <title>Designing for change in complex systems : design considerations for uptime in a transportation system with driverless vehicles</title>
      <link>https://trid.trb.org/View/2388994</link>
      <description><![CDATA[The effects of removing the driver from the transportation system are little explored, but it is reasonable to argue that it will affect the system design, such as how system actors interact, their relationships, and how they need to be organized. The fault-handling system is one crucial subsystem that enables uptime in the transportation system and is the system that provides activities that maintain vehicle health. Such activities can be maintenance, repair, and vehicle monitoring services. However, the fault-handling system provides service centers with experienced technicians, diagnosis and troubleshooting tools, maintenance planning support, and fleet management. Thus, maintenance and repair can be put in the context of a service. Service design methods have been applied in this thesis to generate insights regarding the fault-handling system today and to develop a concept of how a future system for driverless trucks could be designed. The study has involved interviews with system actors, generating patterns, and understanding the system today, and Scania experts have been engaged in creating scenarios. Later, those were used during a workshop to explore the present system and co-create a desired future. Moreover, a prototype was developed to perform interventions. This thesis has two purposes: to explore how design methods can contribute to changing complex socio-technical systems, such as the transportation system, and to explore what design considerations are needed to support uptime when manually driven trucks become driverless. The questions explored are: how can design methods be used to contribute to changes in socio-technical systems, such as the transportation system? How may a system for fault-handling and decision-making be designed to support uptime in a transportation system with driverless vehicles? What is the driver's role concerning uptime in the transportation system?]]></description>
      <pubDate>Mon, 10 Jun 2024 14:05:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2388994</guid>
    </item>
    <item>
      <title>Application of integrated vehicle health management in automated decision-making for driverless vehicles</title>
      <link>https://trid.trb.org/View/2344836</link>
      <description><![CDATA[Vehicles are becoming increasingly complex and are prone to faults and failures, which threaten the dependability of vehicles in terms of availability, reliability, safety, and security. When vehicles are detected with certain types of faults and get into alarm situations, human drivers play a vital role in deciding what strategies and actions to take. Once driverless vehicles are introduced, human drivers' roles in decision-making will no longer exist, which urges new solutions on both technological and managerial levels. This thesis depicts the current human decision-making process by analyzing field study data in the truck industry, which contributes to gaining domain knowledge and identifying research gaps. An integrated vehicle health management scheme is applied to automate this decision-making process by integrating vehicle health state estimation and prediction, resource utilization, and self-adaptive management. To implement this scheme, fault diagnosis and decision-making methods are proposed, and a decision support system is designed. Fault diagnosis is a critical functional module for providing reliable vehicle health state information for decision-making. To address the influence of uncertainties in fault diagnosis, we propose an uncertainty analysis framework and a fault diagnosis method using Bayesian inference. Simulation experiments validate that the proposed method could effectively diagnose the root cause of fault symptoms under environmental uncertainty. A risk-based automated decision-making method is presented, which imitates the human decision-making process. On this basis, a collaborative decision-making method is proposed by considering traffic congestion, which is a currently neglected public concern. Experiment results show that the proposed methods could effectively reduce the economic risk and the risk of traffic congestion.]]></description>
      <pubDate>Tue, 27 Feb 2024 14:26:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2344836</guid>
    </item>
    <item>
      <title>On Board Diagnose (OBD). Analyse der OBD in Bezug auf zukünftig verfügbare Emissionsdaten für die Periodisch Technische Inspektion (PTI) </title>
      <link>https://trid.trb.org/View/1928894</link>
      <description><![CDATA[Die On-Board Diagnostik (OBD) gewinnt zunehmend für die Erfassung der Emissionen von Kraftfahrzeugen an Bedeutung. Mit Blick auf die Anpassung an den aktuellen Stand sowie Weiterentwicklung der Abgasuntersuchung (AU) ist Ziel des Projektes die Ermittlung eines zukünftigen, zusätzlichen Informationsbedarfs für die AU, der über die OBD-Schnittstelle abrufbar ist. Die aktuellen Erfordernisse in Bezug auf die OBD zur Überprüfung der emissionsrelevanten Komponenten sollen mittels einer Umfrage an die Überwachungsinstitutionen erfolgen. Ein weiteres Ziel ist die Betrachtung und Beurteilung unterschiedlichster bereits entwickelter Sensoren der Abgastechnik hinsichtlich der Eignung für eine OBD Anwendung zur AU von Kraftfahrzeugen. Es sollen Sensoren untersucht werden, die gesetzlich limitierte Emissionen in den aktuellen und zukünftigen Richtlinien im Bereich der Abgasnachbehandlung messen können. Ihre Anwendung soll analysiert und hinsichtlich ihrer Einsatzfähigkeit in Bezug auf die Periodisch Technische Inspektion (PTI) beurteilt werden. (A) ABSTRACT IN ENGLISH: On-board diagnostics (OBD) is becoming increasingly important for recording the emissions of motor vehicles. With a view to the adaptation to the current status and further development of the exhaust emission test (AU), the aim of the project is to determine a future, additional information requirement for the AU, which can be retrieved via the OBD interface. The current requirements with regard to the OBD for the inspection of emission-relevant components are to be determined by means of a survey to the monitoring institutions. A further objective is to examine and assess a wide variety of sensors already developed in exhaust technology with regard to their suitability for OBD application for the AU of motor vehicles. Sensors that can measure legally limited emissions in the current and future directives in the field of exhaust gas aftertreatment are to be examined. Their application is to be analysed and assessed with regard to their suitability for use in the Periodic Technical Inspection (PTI). (A)]]></description>
      <pubDate>Fri, 18 Mar 2022 04:38:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1928894</guid>
    </item>
    <item>
      <title>On Board Diagnose (OBD)</title>
      <link>https://trid.trb.org/View/1928886</link>
      <description><![CDATA[Durch die Weiterentwicklung der Motoren und der Abgasnachbehandlung ist eine veränderte Emissionssituation bei Kraftfahrzeugen entstanden. Hierzu zählen unter anderem die Stickoxidnachbehandlungssysteme bei Dieselfahrzeugen. Die Prüfung und Steuerung der Verbrennung und der Abgasnachbehandlungssysteme erfolgt zunehmend mit Sensoren und Plausibilitätsanalysen, welche unter anderem über Diagnosejobs validiert werden, die über die On-Board Diagnostik (OBD) eingeleitet werden. Die OBD gewinnt damit zunehmend für die Erfassung der Emissionen an Bedeutung. Mit Blick auf die Anpassung an den aktuellen Stand sowie die Weiterentwicklung der Abgasuntersuchung (AU) ist das Ziel dieses Forschungsprojekts, durch eine weit gefasste Recherche einen Überblick über die OBD im Bereich der Emissionsmessung zu erarbeiten. Dieser Überblick betrifft den Status Quo der aktuellen Richtlinien, Verordnungen und die praktische Ausführung in den am Markt befindlichen Fahrzeugen. Des Weiteren sollen durch die Studie die digitalen Übertragungsmöglichkeiten und das damit verbundene Entwicklungspotenzial für die Periodisch-Technische Inspektion (PTI) benannt werden. (A) ABSTRACT IN ENGLISH: The further development of engines and exhaust gas aftertreatment has resulted in a changed emission situation for motor vehicles. This includes, among other things, the nitrogen oxide aftertreatment systems in diesel vehicles. The testing and control of combustion and exhaust aftertreatment systems is increasingly carried out with sensors and plausibility analyses, which are validated, among other things, by diagnostic jobs initiated via on-board diagnostics (OBD). OBD is thus becoming increasingly important for the recording of emissions. In view of the adaptation to the current status as well as the further development of the exhaust emission test (AU), the aim of this research project is to develop an overview of OBD in the field of emission measurement by means of a broad research. This overview concerns the status quo of the current directives, regulations and the practical implementation in the vehicles on the market. Furthermore, the study is to identify the digital transmission possibilities and the associated development potential for the Periodic Technical Inspection (PTI). (A)]]></description>
      <pubDate>Fri, 18 Mar 2022 04:38:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1928886</guid>
    </item>
    <item>
      <title>LOBSTR – learning on-board signals for timely reaction</title>
      <link>https://trid.trb.org/View/1894966</link>
      <description><![CDATA[The project objective is to investigate the possibility to apply and implement real time anomaly detection on temporal multivariate signals on-board the vehicle with knowledge sharing for fault detection. Within the truck industry, severe faults are rare and very expensive to reproduce on test labs. This makes it difficult to go with traditional classification methods for fault detection. Anomaly detection is identification of rare observations that differ significantly from the majority of the data set. In this case, normal functional operations of the vehicle would be the majority of the data set, which makes this a perfect approach for fault detection. Learning and knowledge sharing between vehicles are important as one vehicle might not expand the whole space of possible operations. A new encounter might look suspicious for one vehicle but it might be a daily routine for another vehicle, hence the collective experience sharing is an important aspect of anomaly detection. In this project, several different anomaly detection methods on recorded time series data were investigated. These recorded data include normal driving of a vehicle and faulty drives with purposely injected fault.]]></description>
      <pubDate>Wed, 01 Dec 2021 14:49:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1894966</guid>
    </item>
    <item>
      <title>Machine learning models for predictive maintenance</title>
      <link>https://trid.trb.org/View/1894872</link>
      <description><![CDATA[The amount of goods produced and transported around the world each year increases and heavy-duty trucks are an important link in the logistic chain. To guarantee reliable delivery a high degree of availability is required, i.e., avoid standing by the road unable to continue the transport mission. Unplanned stops by the road do not only cost due to the delay in delivery, but can also lead to damaged cargo. Vehicle downtime can be reduced by replacing components based on statistics of previous failures. However, such an approach is both expensive due to the required frequent visits to a workshop and inefficient as many components from the vehicles in the fleet are still operational. A prognostic method, allowing for vehicle individualized maintenance plans, therefore poses a significant potential in the automotive field. The prognostic method estimates component degradation and remaining useful life based on recorded data and how the vehicle has been operated. Lead-acid batteries is a part of the electrical power system in a heavy-duty truck, primarily responsible for powering the starter motor but also powering auxiliary units, e.g., cabin heating and kitchen equipment, which makes the battery a vital component for vehicle availability. Developing physical models of battery degradation is a difficult process which requires access to battery health sensing that is not available in the given study as well a detailed knowledge of battery chemistry. An alternative approach, considered in this work, is data-driven methods based on large amounts of logged data describing vehicle operation conditions. In the use-case studied, recorded data is not closely related to battery health which makes battery prognostic challenging. Data is collected during infrequent and non-equidistant visits to a workshop and there are complex dependencies between variables in the data. The main aim of this work has been to develop a framework and methods for estimating lifetime of lead-acid batteries using data-driven methods for condition-based maintenance. The methodology is general and can be applicable for prognostics of other components.]]></description>
      <pubDate>Wed, 01 Dec 2021 14:45:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1894872</guid>
    </item>
    <item>
      <title>On the issue of the PEMFC operating fault identification: Generic analysis tool based on voltage pointwise singularity strengths</title>
      <link>https://trid.trb.org/View/1681528</link>
      <description><![CDATA[The purpose of this article is to study the portability of a non-intrusive and free of any external / internal disturbance diagnosis tool devoted to the monitoring of the State of Health (SoH) of PEM Fuel Cell (PEMFC) stack. The tool is based on a thorough analysis of the stack voltage signal using a multifractal formalism and wavelet leaders. It offers well-suited signatures indicators on the SoH of the Fuel Cell. Some relevant descriptors extracted from these patterns (singularity features) are used in the frame of Machine Learning approaches to allow the PEMFC fault identification. The proposed diagnosis strategy is evaluated with two different PEMFC stacks. The first one is designed for automotive applications and the second one is dedicated to stationary use (micro combined heat and power - µCHP application). The classification results obtained for the both stacks indicate that the proposed PEMFC diagnosis tool allows identifying simple operating faults as well as more complicated operating situations combining several fault types.]]></description>
      <pubDate>Tue, 28 Jan 2020 16:13:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1681528</guid>
    </item>
    <item>
      <title>A diagnostic method for evaluating the condition index of cement-stabilised base using T-S fuzzy neural network</title>
      <link>https://trid.trb.org/View/1645917</link>
      <description><![CDATA[This paper presents a novel diagnostic model for evaluating the damage condition of a cement-stabilised base course using a T-S fuzzy neural network (FNN). An evaluation criterion for core damage rating, base core condition index (BCCI), was established based on the distress features observed in 369 core samples that had been collected from asphalt concrete pavements with cement-stabilised base courses. The core samples of cement-stabilised macadam (CSM) were classified into five levels according to the evaluation criterion. Ten parameters were chosen as inputs to establish non-linear mapping relationships in an FNN model. These input parameters include pavement distresses’ characteristics (crack depth, breadth and lumpiness), Pavement Surface Condition Index (PCI), Riding Quality Index (RQI), Pavement Structure Strength Index (PSSI), Cumulative Equivalent Single Axle Loads (ESALs), thickness of asphalt concrete (AC) layers, annual temperature difference and average annual precipitation. Out of the 369 samples, 169 field cores were used in the FNN model for recursive training, and the parameters for the neural network structure were optimised until the errors between the network outputs and the expected outputs were minimised. The established FNN model was then used for the calculations of predicted quantitative results for remaining sections. The calculation results showed the logical reasoning capability of the T-S fuzzy system and the quantitative data processing capability of the neural network (NN). The comparisons between the objectively predicted results with the subjectively evaluated scores of the testing samples showed a prediction accuracy of 88.4%. The objectively predicted results calculated by the FNN model were also compared with the measurements taken using a Ground Penetrating Radar (GPR). The two groups of results showed reasonable similarity, which again indicated the effectiveness of the developed FNN method.]]></description>
      <pubDate>Mon, 26 Aug 2019 15:42:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1645917</guid>
    </item>
    <item>
      <title>Effects of Environmental Parameters on Real-World NOx Emissions and Fuel Consumption for Heavy-Duty Diesel Trucks Using an OBD Approach</title>
      <link>https://trid.trb.org/View/1562122</link>
      <description><![CDATA[OBD (On-Board Diagnostic) test system is applied to research influences of environmental parameters (altitude and environment temperature) on real-world NOx emission and fuel consumption for heavy-duty diesel trucks in this paper. The research results indicate that altitude and environment temperature have great influence on NOx emission rate and fuel consumption. High altitude in range of 3000~4000 m results in NOx emission rate is lower than low and moderate temperature because of air intake amount decreasing. However the fuel consumption rate is higher than lower altitude because altitude influences real-time changes of air inflow and combustion conditions in the cylinder of the engine. NOx emission rate and fuel consumption is more stable at different vehicle speed, VSP and RPM at high altitude, and NOx emission rate fluctuate dramatically at low and moderate altitude. The fuel consumption rate is higher at 10~20 °C than that at lower and higher temperature. The environment temperature of 20~35 °C provides beneficial conditions for NOx production and deteriorates emission, and the environment temperature of -10~10 °C provides oxygen enrichment environment because of low temperature and high air density, so increases in production amount of NOx. Effects of altitude and environment temperature on NOx emission rate and fuel consumption rate show an opposite tendency. Compared with fuel consumption rate, NOx emission rate is more sensitive to vehicle speed, VSP and rotating speed.       ]]></description>
      <pubDate>Thu, 18 Jul 2019 16:39:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1562122</guid>
    </item>
    <item>
      <title>Ergonomic Data Measuring System for Driver-Pedals Interaction</title>
      <link>https://trid.trb.org/View/1430629</link>
      <description><![CDATA[This paper presents the design and development of an ergonomic data measurement system for driver–pedals interaction. The work focuses in particular on the actuation of the acceleration and brake pedals, and aims to support the development of a deeper understanding of the factors influencing the driving comfort associated with the right leg. The ergonomic data measurement system integrates five subsystems: an electro-goniometry system and a pressure-pads system to monitor driver’s positioning and movements, an electromiography system to observe the muscular activity of the lower leg, the vehicle on-board diagnostic system, a GPS system and an audio-visual system for providing environment and driving situation information. A validation exercise involving a series of test drive events confirmed the system capability to record meaningful objective comfort data which can differentiate between driving postures and styles.       ]]></description>
      <pubDate>Fri, 07 Jun 2019 17:29:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1430629</guid>
    </item>
    <item>
      <title>Smartphone-Based Pothole Detection Utilizing Artificial Neural Networks</title>
      <link>https://trid.trb.org/View/1607417</link>
      <description><![CDATA[Roadway pavement maintenance to the preferred level of serviceability comprises one of the most challenging problems faced by civil and transportation engineers, with regard to transport infrastructure management. This paper presents a study on the detection of roadway pavement anomalies by use of smartphone sensors and on-board diagnostic (OBD-II) devices, which can lead to low-cost roadway infrastructure assessment. The proposed approach, which, in addition to smartphone sensors, also utilizes artificial neural network (ANN) techniques in the analysis, captures a vehicle’s interaction with a roadway pavement while the vehicle is moving, and utilizes the observed interaction patterns for the detection of potholes in the pavement. The method utilizes four metrics in the analysis and shows a detection accuracy of about 90%. Preliminary results on the inclusion of additional roadway defects in the analysis and on the ability of the method to distinguish between potholes and other pavement defects (e.g., patches, local upheavals, rutting, and corrugation) have been positive. The study’s results confirm the value of smartphone sensors in the low-cost (and eventually crowdsourced) detection of potholes.]]></description>
      <pubDate>Thu, 23 May 2019 16:27:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1607417</guid>
    </item>
    <item>
      <title>CAN-Bus Remote Monitoring: Standalone CAN Sensor Reading and Automotive Diagnostics</title>
      <link>https://trid.trb.org/View/1592020</link>
      <description><![CDATA[A vehicle may be a font of data for some applications in safety, maintenance, and entertainment systems, once its electronic control units are connected to each other by a Controller Area Network (CAN) bus. By plugging a compatible device on the vehicle onboard diagnostics interface, reading raw data or conducting automotive diagnostics by International Standardization Organization 15765 and Society of Automotive Engineers J1979 is possible. The usual low-cost CAN data acquisition devices do not allow the connection to a cloud service for remote monitoring. Looking at this issue, this work proposes a low-cost NodeMCU CAN shield for data acquisition which is able to read the CAN frame of a Steering Angle Sensor, in Scenario 1, and standardized information from a vehicle such as its speed, identification number, and engine coolant temperature by automotive diagnostics, in Scenario 2. Both vehicle and standalone sensor data will be made available to the system user by the Thinger.io® Internet-of-Things platform by means of a smartphone 4G internet connection, aiming at remote CAN sensor diagnostics and vehicle monitoring.]]></description>
      <pubDate>Thu, 18 Apr 2019 11:06:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1592020</guid>
    </item>
    <item>
      <title>Management of the reliability of intelligent vehicles as a method to improve traffic safety</title>
      <link>https://trid.trb.org/View/1577443</link>
      <description><![CDATA[World trends in the area of engineering and technology are associated with intellectualization of both the processes and the technical systems. In many cases, exclusion of a human from the control loop provides certain advantages, but at the same time causes a number of problems. Large systems encounter a security issue. This also applies to transport systems for which the development of “unmanned vehicles” has become a logical result of implementation of the Intelligent Transport Systems (ITS) as a system strategy. Transition from creation of driver assistance systems to development of semi-autonomous and unmanned vehicles can be explained by desire of developers to ensure stability and safety of the transport system. However, according to analytical forecasts, while gradual intellectualization of vehicles will result in decreased role of human factor in statistics of road traffic accidents, it will also lead to the growth of their number due to technical malfunction. Therefore, the importance of measures to prevent sudden vehicle failures increases. The article shows the importance of intelligent on-board systems for improving vehicle diagnostic systems. A conceptual scheme of decision support system for managing vehicle service and its interactions with interactive diagnostic system are presented. A scheme of interaction of components of the on-board diagnostic system is provided, and the interrelation of the structural and diagnostic parameters determining the state of the components and systems of the vehicle is shown. Such solutions enable improving the service system and optimizing its operation. It is shown that only system solutions in this field will increase reliability and faultless operation of autonomous vehicles, and will ensure their smooth and failure-free operation.]]></description>
      <pubDate>Mon, 18 Feb 2019 16:34:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1577443</guid>
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