5,918 research outputs found

    Data mining for vehicle telemetry

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    This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height

    Data mining for vehicle telemetry

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    This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labelling problems: Road Type (A, B, C and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, namely, signal selection, feature extraction, and feature selection. The selection methods used include Principal Components Analysis (PCA) and Mutual Information (MI), which are used to determine the relevance and redundancy of extracted features, and are performed in various combinations. Finally, as there is an inherent bias towards certain road and carriageway labellings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension heigh

    Intelligent fault detection and classification based on hybrid deep learning methods for Hardware-in-the-Loop test of automotive software systems

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    Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%

    Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

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    Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

    Study of a mobile app interface supporting behaviour change in electric vehicles use

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    This paper is based on a study aimed at examining how historical data on energy consumptions in electric vehicles could be (re)designed by taking into account Kahneman’s theories (in particular “Fast and Slow thinking”, Kahneman, 2011 and “Prospect Theory”, Kahneman & Tversky, 1979): they should be applied to support driver’s behaviour changing, in particular eco-driving, by focusing on loss rather than gain. This paper describes i) the design of an HMI concept as part of a mobile app, providing information on energy consumption in two versions (loss/gain of recovered km). It is based on a literature analysis on energy use behaviour and eco-driving and an analysis of information systems for home energy use and instrument clusters already in use in electric vehicles (Fully Electric - FEV and Hybrid - HEV, ii) a heuristic evaluation of the HMI concept; ii) outcomes from a co-design session

    Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

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    Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

    Classification of potential electric vehicle purchasers: A machine learning approach

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    13 p.Among the many approaches towards fuel economy, the adoption of electric vehicles (EV) may have the greatest impact. However, existing studies on EV adoption predict very different market evolutions, which causes a lack of solid ground for strategic decision making. New methodological tools, based on Artificial Intelligence, might offer a different perspective. This paper proposes supervised Machine Learning (ML) techniques to identify key elements in EV adoption, comparing different ML methods for the classification of potential EV purchasers. Namely, Support Vector Machines, Artificial Neural Networks, Deep Neural Networks, Gradient Boosting Models, Distributed Random Forests, and Extremely Randomized Forests are modeled utilizing data gathered on users’ inclinations towards EV. Although a Support Vector Machine with polynomial kernel slightly outperforms the other algorithms, all of them exhibit comparable predictability, implying robust findings. Further analysis provides evidence that having only partial information (e.g. only socioeconomic variables) has a significant negative impact on model performance, and that the synergy across several types of variables leads to higher accuracy. Finally, the examination of misclassified observations reveals two well-differentiated groups, unveiling the importance that the profiling of potential purchaser may have for marketing campaigns as well as for public agencies that seek to promote EV adoption

    Implementation and in-depth analyses of a battery-supercapacitor powered electric vehicle (E-Kancil)

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    This thesis contributes to the research issue pertaining to the management of multiple energy sources on-board a pure electric vehicle; particularly the energy dense traction battery and the power dense supercapacitor or ultracapacitor. This is achieved by analysing real world drive data on the interaction between lead acid battery pack and supercapacitor module connected in parallel while trying to fulfil the load demands of the vehicle. The initial findings and performance of a prototype electric vehicle conversion of a famous Malaysian city car; the perodual kancil, is presented in this thesis. The 660 cc compact city car engine was replaced with a brushless DC motor rated at 8KW continuous and 20KW peak. The battery pack consists of eight T105 Trojan 6V, 225 Ah deep cycle lead acid battery which builds up a voltage of 48V. In addition to this, a supercapacitor module (165F, 48V) is connected in parallel using high power contactors in order to investigate the increase in performance criteria such as acceleration, range, battery life etc. which have been proven in various literatures via simulation studies. A data acquisition system is setup in order to collect real world driving data from the electric vehicle on the fly along a fixed route. Analysis of collected driving data is done using MATLAB software and comparison of performance of the electric vehicle with and without supercapacitor module is made. Results show that with a parallel connection, battery life and health is enhanced by reduction in peak currents of up to 49%. Peak power capabilities of the entire hybrid source increased from 9.5KW to 12.5KW. A 41% increase in range per charge was recorded. The author of this work hopes that by capitalizing on the natural peak power buffering capabilities of the supercapacitor, a cost effective energy management system can be designed in order to utilize more than 23.6% of the supercapacitor energy

    Transportation System Performance and Traveler Behavior in the Context of a Systemwide Shock: Applications of Data Science Toward a Sustainable Future

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    The COVID-19 pandemic, a systemwide shock, has left a long-lasting and significant impact on transportation systems. It has contributed to a shift in travel behavior, with many people turning to work from home (WFH) and online shopping. This shift has led to a reduction in vehicular travel. However, the pandemic witnessed increased crash fatalities despite a reduction in overall crashes, disproportionately affecting disadvantaged communities (DACs). The main question arising from these pandemic-related issues is what we can learn to improve transportation systems and shape future travel behavior. Therefore, this dissertation aims to investigate how the transportation system changed during COVID-19 and explore the future implications while examining the travel behavior, technology adoption behavior, and road safety aspects in DACs compared with non-DACs during COVID-19. As such, this dissertation first explores the interaction between WFH, online shopping, and in-person shopping behaviors, revealing nuanced relationships that have evolved amidst the pandemic. Second, comprehensive safety data are utilized to dissect why crash fatalities increased during COVID-19. Third, transportation safety in DACs is investigated by leveraging safety data covering COVID-19 periods and the comprehensive DAC indicators developed by the US Department of Transportation. Fourth, DACs’ shopping behavior during COVID-19 is analyzed by focusing on the interplay of emerging online delivery components (retail, grocery, and food) and in-person activities. Finally, the study compares technology adoption behaviors between DACs and non-DACs by exploring infrastructure and socio-economic barriers. Methodologically speaking, this dissertation employs various state-of-the-art statistical and explainable artificial intelligence techniques. Overall findings indicate that compared to pre-COVID-19, the surge in WFM and e-commerce trends was associated with a substantial reduction in physical shopping trips during COVID-19. Speeding and reckless behaviors were strongly associated with the increased road fatalities. DACs experienced heightened adversity than non-DACs, associated with a higher rate of fatal crashes (an increase of 8% to 57%). Online orders were considerably less frequent in DACs than non-DACs (2% to 7%), emphasizing disparity in digital infrastructure. Additionally, technology adoption rates were significantly lower in DACs. These findings underscore the importance of better preparedness and planning for such communities to be equipped to handle future systemic shocks
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