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description Publicationkeyboard_double_arrow_right Article , Preprint 2024Publisher:Elsevier BV Funded by:EC | interACTEC| interACTFengjiao Zou; Jennifer Ogle; Weimin Jin; Patrick Gerard; Daniel Petty; Andrew Robb;One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrian crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane (TWLTL), and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality to simulate an urban unmarked midblock environment where pedestrians interact with AVs. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrian unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. But only the roadway centerline features significantly impact the walking time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle. For middle waiting time, yellow/blue signals had the most impact on the median road type and the least on the undivided road. Demographics, past behaviors, and walking exposure are also explored. Older individuals tend to wait longer, and pedestrian past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior.
arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part F Traffic Psychology and BehaviourArticle . 2024 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part F Traffic Psychology and BehaviourArticle . 2024 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trf.2023.11.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023Publisher:Elsevier BV Funded by:EC | DIT4TraMEC| DIT4TraMAuthors: Caio Vitor Beojone; Nikolas Geroliminis;Caio Vitor Beojone; Nikolas Geroliminis;Dynamic network-level models directly addressing ride-sourcing services can support the development of efficient strategies for both congestion alleviation and promotion of more sustainable mobility. Recent developments presented models focusing on ride-hailing (solo rides), but no work addressed ridesplitting (shared rides) in dynamic contexts. Here, we sought to develop a dynamic aggregated traffic network model capable of representing ride-sourcing services and background traffic in a macroscopic multi-region urban network. We combined the Macroscopic Fundamental Diagram (MFD) with detailed state-space and transition descriptions of background traffic and ride-sourcing vehicles in their activities to formulate mass conservation equations. Accumulation-based MFD models might experience additional errors due to the variation profile of trip lengths, e.g., when vehicles cruise for passengers. We integrate the so-called M-model that utilizes the total remaining distance to capture dynamics of regional and inter-regional flows and accumulations for different vehicle (private or ride-sourcing) states. This aggregated model is capable to reproduce the dynamics of complex systems without using resource-expensive simulations. We also show that the model can accurately forecast the vehicles' conditions in near-future predictions. Later, a comparison with benchmark models showed lower errors in the proposed model in all states. Finally, we evaluated the model's robustness to noises in its inputs, and forecast errors remained below 15% even where inputs were 20% off the actual values for ride-sourcing vehicles. The development of such a model prepares the path for developing real-time feedback-based management policies such as priority-based perimeter control or repositioning strategies for idle ride-sourcing vehicles and developing regulations over ride-sourcing in congested areas.
arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part B MethodologicalArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part B MethodologicalArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trb.2023.102821&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 ItalyPublisher:Elsevier BV Funded by:NSERC, EC | FOCETANSERC ,EC| FOCETANejati, Shiva; Sorokin, Lev; Safin, Damir; Formica, Federico; Mahboob, Mohammad Mahdi; Menghi, Claudio;Surrogate-assisted search-based testing (SA-SBT) aims to reduce the computational time for testing compute-intensive systems. Surrogates enhance testing techniques by improving test case generation focusing the testing budget on the most critical portions of the input domain. In addition, they can serve as approximations of the system under test (SUT) to predict tests' results instead of executing the tests on compute-intensive SUTs. This article reflects on the existing SA-SBT techniques, particularly those applied to system-level testing and often facilitated using simulators or complex test beds. Our objective is to synthesize different heuristic algorithms and evaluation methods employed in existing SA-SBT techniques and present a comprehensive view of SA-SBT solutions. In addition, by critically reviewing our previous work on SA-SBT, we aim to identify the limitations in our proposed algorithms and evaluation methods and to propose potential improvements. We present a taxonomy that categorizes and contrasts existing SA-SBT solutions and highlights key research gaps. To identify the evaluation challenges, we conduct two replication studies of our past SA-SBT solutions: One study uses industrial advanced driver assistance system (ADAS) and the other relies on a Simulink model benchmark. We compare our results with those of the original studies and identify the difficulties in evaluating SA-SBT techniques, including the impact of different contextual factors on results generalization and the validity of our evaluation metrics. Based on our taxonomy and replication studies, we propose future research directions, including re-considerations in the current evaluation metrics used for SA-SBT solutions, utilizing surrogates for fault localization and repair in addition to testing, and creating frameworks for large-scale experiments by applying SA-SBT to multiple SUTs and simulators. Comment: Submitted to the Information and Software Technology Journal
arXiv.org e-Print Ar... arrow_drop_down Archivio Istituzionale Università di BergamoArticle . 2023Data sources: Archivio Istituzionale Università di Bergamohttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Archivio Istituzionale Università di BergamoArticle . 2023Data sources: Archivio Istituzionale Università di Bergamohttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Research , Other literature type , Preprint 2023 SwitzerlandPublisher:Elsevier BV Funded by:EC | DIT4TraMEC| DIT4TraMAuthors: Dimitrios Tsitsokas; Anastasios Kouvelas; Nikolas Geroliminis;Dimitrios Tsitsokas; Anastasios Kouvelas; Nikolas Geroliminis;Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dynamic traffic characteristics. Max Pressure (MP) distributed control gained significant popularity due to its theoretically proven ability of throughput maximization under specific assumptions. However, its effectiveness is questionable in over-saturated conditions, while network-scale implementation is often practically limited due to high instrumentation cost, which increases proportionally to the number of controlled intersections. Perimeter control (PC) based on the concept of Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated control strategy that regulates exchange flows between homogeneously congested regions, with the objective of maximizing traffic system performance and prevent over-saturation. However, homogeneity assumption is hardly realistic under congested conditions, which can compromise PC effectiveness. In this paper, network-wide parallel application of PC and MP strategies embedded in a two-layer control framework is evaluated in a macroscopic simulation environment. With the aim of reducing implementation cost of network-wide MP without significant performance drop, we propose a critical node identification algorithm that is based on node traffic characteristics and assess partial MP deployment to the most critical nodes. An enhanced version of Store-and-forward dynamic traffic paradigm incorporating finite queues and spill-back consideration is used to test different configurations of the two-layer framework, as well as each layer individually, for a real large-scale network, in moderate and highly congested conditions. Results show that: (i) combination of MP and PC outperforms individual layer application in almost all cases for both demand scenarios tested; (ii) MP control in critical node sets formed by the proposed strategy leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under stochastic demand fluctuations of up to 20% of mean. Transportation Research Part C: Emerging Technologies, 152 ISSN:0968-090X
Research Collection arrow_drop_down Transportation Research Part C Emerging TechnologiesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationshttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trc.2023.104128&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Research Collection arrow_drop_down Transportation Research Part C Emerging TechnologiesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationshttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trc.2023.104128&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:UKRI | GaN Sense, EC | Hexa-XUKRI| GaN Sense ,EC| Hexa-XBin Han; Mohammad Asif Habibi; Bjoern Richerzhagen; Kim Schindhelm; Florian Zeiger; Fabrizio Lamberti; Filippo Gabriele Pratticò; Karthik Upadhya; Charalampos Korovesis; Ioannis-Prodromos Belikaidis; Panagiotis Demestichas; Siyu Yuan; Hans D. Schotten;handle: 11583/2982926
Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios. Comment: Accepted for publication in IEEE Open Journal of Vehicular Technology
Publications Open Re... arrow_drop_down IEEE Open Journal of Vehicular TechnologyArticle . 2023 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Publications Open Re... arrow_drop_down IEEE Open Journal of Vehicular TechnologyArticle . 2023 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 NetherlandsPublisher:Elsevier BV Funded by:EC | SAFE-UPEC| SAFE-UPAuthors: Sánchez, Manuel Muñoz; Silvas, Emilia; Elfring, Jos; van de Molengraft, René;Sánchez, Manuel Muñoz; Silvas, Emilia; Elfring, Jos; van de Molengraft, René;Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic behavior when presented with previously unseen inputs. In this work, two environment-aware models (MotionCNN and MultiPath++) and two common baselines (Constant Velocity and an LSTM) are benchmarked for robustness against various perturbations that simulate functional insufficiencies observed during model deployment in a vehicle: unavailability of road information, late detections, and noise. Results show significant performance degradation under the presence of these perturbations, with errors increasing up to +1444.8\% in commonly used trajectory prediction evaluation metrics. Training the models with similar perturbations effectively reduces performance degradation, with error increases of up to +87.5\%. We argue that despite being an effective mitigation strategy, data augmentation through perturbations during training does not guarantee robustness towards unforeseen perturbations, since identification of all possible on-road complications is unfeasible. Furthermore, degrading the inputs sometimes leads to more accurate predictions, suggesting that the models are unable to learn the true relationships between the different elements in the data.
IFAC-PapersOnLine arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert IFAC-PapersOnLine arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ifacol.2023.10.1256&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2022 DenmarkPublisher:Elsevier BV Funded by:EC | INTERPRETER, EC | ERANet SmartGridPlusEC| INTERPRETER ,EC| ERANet SmartGridPlusMüller, Nils; Chevalier, Samuel; Heinrich, Carsten; Heussen, Kai; Ziras, Charalampos;The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading to network violations. DSOs require reliable and accurate low-voltage state estimation (LVSE) to improve awareness and mitigate such events. However, the influence of flexibility activations on LVSE has not been addressed yet. It remains unclear if flexibility-induced uncertainty can be reliably quantified to enable robust DSO decision-making. In this work, uncertainty quantification in LVSE is systematically investigated for multiple scenarios of input availability and flexibility utilization, using real data. For that purpose, a Bayesian neural network (BNN) is compared to quantile regression. Results show that frequent flexibility activations can significantly deteriorate LVSE performance, unless secondary substation measurements are available. Moreover, it is demonstrated that the BNN captures flexibility-induced voltage drops by dynamically extending the prediction interval during activation periods, and that it improves interpretability regarding the cause of uncertainty. Submitted to the 22nd Power Systems Computation Conference (PSCC 2022)
Electric Power Syste... arrow_drop_down Online Research Database In TechnologyArticle . 2022Data sources: Online Research Database In Technologyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert Electric Power Syste... arrow_drop_down Online Research Database In TechnologyArticle . 2022Data sources: Online Research Database In Technologyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Other literature type , Article 2022 SpainPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | BRAVEEC| BRAVEAuthors: Ruben Izquierdo Gonzalo; Carlota Salinas Maldonado; Javier Alonso Ruiz; Ignacio Parra Alonso; +2 AuthorsRuben Izquierdo Gonzalo; Carlota Salinas Maldonado; Javier Alonso Ruiz; Ignacio Parra Alonso; David Fernandez Llorca; Miguel Angel Sotelo;handle: 10017/59771
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess the required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow the evaluation of safety with real behaviors for critical and edge cases nor the evaluation of the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this article, we present and analyze the results of physical tests on the proving grounds of several predictive systems in automated driving functions developed within the framework of the BRidging Gaps for the Adoption of Automated VEhicles (BRAVE) project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions. Comunidad de Madrid; Ministerio de Ciencia e Innovación; Unión Europea Programa Horizon2020
arXiv.org e-Print Ar... arrow_drop_down Biblioteca Digital de la Universidad de AlcaláOther literature type . 2022License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de AlcaláIEEE Intelligent Transportation Systems MagazineArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Biblioteca Digital de la Universidad de AlcaláOther literature type . 2022License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de AlcaláIEEE Intelligent Transportation Systems MagazineArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mits.2022.3170649&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2022 ItalyPublisher:Elsevier BV Funded by:EC | AI4EU, EC | AI4MediaEC| AI4EU ,EC| AI4MediaCiampi, Luca; Gennaro, Claudio; Carrara, Fabio; Falchi, Fabrizio; Vairo, Claudio; Amato, Giuseppe;This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed multi-camera system is capable of automatically estimate the number of cars present in the entire parking lot directly on board the edge devices. It comprises an on-device deep learning-based detector that locates and counts the vehicles from the captured images and a decentralized geometric-based approach that can analyze the inter-camera shared areas and merge the data acquired by all the devices. We conduct the experimental evaluation on an extended version of the CNRPark-EXT dataset, a collection of images taken from the parking lot on the campus of the National Research Council (CNR) in Pisa, Italy. We show that our system is robust and takes advantage of the redundant information deriving from the different cameras, improving the overall performance without requiring any extra geometrical information of the monitored scene. Submitted to Expert Systems With Applications
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 23visibility views 23 download downloads 29 Powered bymore_vert ISTI Open Portal arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article , Conference object 2022Publisher:IEEE Publicly fundedFunded by:EC | IoTCrawler, EC | ACTIVAGEEC| IoTCrawler ,EC| ACTIVAGEAuthors: Agarwal, Rachit; Elsaleh, Tarek; Tragos, Elias;Agarwal, Rachit; Elsaleh, Tarek; Tragos, Elias;Testing and experimentation are crucial for promoting innovation and building systems that can evolve to meet high levels of service quality. IoT data that belong to users and from which their personal information can be inferred are frequently shared in the background of IoT systems with third parties for experimentation and building quality services. This data sharing raises privacy concerns especially since in most cases the data are gathered and shared without the user's knowledge or explicit consent or for different purposes than the one for which the data were initially gathered. With the introduction of GDPR, IoT systems and experimentation platforms that federate data from different deployments, testbeds and data providers must be privacy-preserving. The wide adoption of IoT applications in scenarios ranging from smart cities to Industry 4.0 has raised concerns with respect to the privacy of users' data collected using IoT devices. Many experimental smart city applications are also using crowdsourcing data. Inspired by the GDPR requirements, we propose an IoT ontology built using available standards that enhances privacy, enables semantic interoperability between IoT deployments and supports the development of privacy-preserving experimental IoT applications. On top, we propose recommendations on how to efficiently use the ontology within IoT testbed and federating platforms. Our ontology is validated for different quality assessment criteria using standard validation tools. We focus on "experimentation" without loss of generality, because it covers scenarios from both research and industry, that are directly linked with innovation.
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.1109/wf-iot...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.1109/wf-iot...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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description Publicationkeyboard_double_arrow_right Article , Preprint 2024Publisher:Elsevier BV Funded by:EC | interACTEC| interACTFengjiao Zou; Jennifer Ogle; Weimin Jin; Patrick Gerard; Daniel Petty; Andrew Robb;One of the main challenges autonomous vehicles (AVs) will face is interacting with pedestrians, especially at unmarked midblock locations where the right-of-way is unspecified. This study investigates pedestrian crossing behavior given different roadway centerline features (i.e., undivided, two-way left-turn lane (TWLTL), and median) and various AV operational schemes portrayed to pedestrians through on-vehicle signals (i.e., no signal, yellow negotiating indication, and yellow/blue negotiating/no-yield indications). This study employs virtual reality to simulate an urban unmarked midblock environment where pedestrians interact with AVs. Results demonstrate that both roadway centerline design features and AV operations and signaling significantly impact pedestrian unmarked midblock crossing behavior, including the waiting time at the curb, waiting time in the middle of the road, and the total crossing time. But only the roadway centerline features significantly impact the walking time. Participants in the undivided scene spent a longer time waiting at the curb and walking on the road than in the median and TWLTL scenes, but they spent a shorter time waiting in the middle. Compared to the AV without a signal, the design of yellow signal significantly reduced pedestrian waiting time at the curb and in the middle. But yellow/blue significantly increased the pedestrian waiting time. Interaction effects between roadway centerline design features and AV operations and signaling are significant only for waiting time in the middle. For middle waiting time, yellow/blue signals had the most impact on the median road type and the least on the undivided road. Demographics, past behaviors, and walking exposure are also explored. Older individuals tend to wait longer, and pedestrian past crossing behaviors and past walking exposures do not significantly impact pedestrian walking behavior.
arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part F Traffic Psychology and BehaviourArticle . 2024 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trf.2023.11.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part F Traffic Psychology and BehaviourArticle . 2024 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trf.2023.11.003&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023Publisher:Elsevier BV Funded by:EC | DIT4TraMEC| DIT4TraMAuthors: Caio Vitor Beojone; Nikolas Geroliminis;Caio Vitor Beojone; Nikolas Geroliminis;Dynamic network-level models directly addressing ride-sourcing services can support the development of efficient strategies for both congestion alleviation and promotion of more sustainable mobility. Recent developments presented models focusing on ride-hailing (solo rides), but no work addressed ridesplitting (shared rides) in dynamic contexts. Here, we sought to develop a dynamic aggregated traffic network model capable of representing ride-sourcing services and background traffic in a macroscopic multi-region urban network. We combined the Macroscopic Fundamental Diagram (MFD) with detailed state-space and transition descriptions of background traffic and ride-sourcing vehicles in their activities to formulate mass conservation equations. Accumulation-based MFD models might experience additional errors due to the variation profile of trip lengths, e.g., when vehicles cruise for passengers. We integrate the so-called M-model that utilizes the total remaining distance to capture dynamics of regional and inter-regional flows and accumulations for different vehicle (private or ride-sourcing) states. This aggregated model is capable to reproduce the dynamics of complex systems without using resource-expensive simulations. We also show that the model can accurately forecast the vehicles' conditions in near-future predictions. Later, a comparison with benchmark models showed lower errors in the proposed model in all states. Finally, we evaluated the model's robustness to noises in its inputs, and forecast errors remained below 15% even where inputs were 20% off the actual values for ride-sourcing vehicles. The development of such a model prepares the path for developing real-time feedback-based management policies such as priority-based perimeter control or repositioning strategies for idle ride-sourcing vehicles and developing regulations over ride-sourcing in congested areas.
arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part B MethodologicalArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trb.2023.102821&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Transportation Research Part B MethodologicalArticle . 2023 . Peer-reviewedLicense: Elsevier TDMData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trb.2023.102821&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 ItalyPublisher:Elsevier BV Funded by:NSERC, EC | FOCETANSERC ,EC| FOCETANejati, Shiva; Sorokin, Lev; Safin, Damir; Formica, Federico; Mahboob, Mohammad Mahdi; Menghi, Claudio;Surrogate-assisted search-based testing (SA-SBT) aims to reduce the computational time for testing compute-intensive systems. Surrogates enhance testing techniques by improving test case generation focusing the testing budget on the most critical portions of the input domain. In addition, they can serve as approximations of the system under test (SUT) to predict tests' results instead of executing the tests on compute-intensive SUTs. This article reflects on the existing SA-SBT techniques, particularly those applied to system-level testing and often facilitated using simulators or complex test beds. Our objective is to synthesize different heuristic algorithms and evaluation methods employed in existing SA-SBT techniques and present a comprehensive view of SA-SBT solutions. In addition, by critically reviewing our previous work on SA-SBT, we aim to identify the limitations in our proposed algorithms and evaluation methods and to propose potential improvements. We present a taxonomy that categorizes and contrasts existing SA-SBT solutions and highlights key research gaps. To identify the evaluation challenges, we conduct two replication studies of our past SA-SBT solutions: One study uses industrial advanced driver assistance system (ADAS) and the other relies on a Simulink model benchmark. We compare our results with those of the original studies and identify the difficulties in evaluating SA-SBT techniques, including the impact of different contextual factors on results generalization and the validity of our evaluation metrics. Based on our taxonomy and replication studies, we propose future research directions, including re-considerations in the current evaluation metrics used for SA-SBT solutions, utilizing surrogates for fault localization and repair in addition to testing, and creating frameworks for large-scale experiments by applying SA-SBT to multiple SUTs and simulators. Comment: Submitted to the Information and Software Technology Journal
arXiv.org e-Print Ar... arrow_drop_down Archivio Istituzionale Università di BergamoArticle . 2023Data sources: Archivio Istituzionale Università di Bergamohttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen 2 citations 2 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Archivio Istituzionale Università di BergamoArticle . 2023Data sources: Archivio Istituzionale Università di Bergamohttps://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.infsof.2023.107286&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Research , Other literature type , Preprint 2023 SwitzerlandPublisher:Elsevier BV Funded by:EC | DIT4TraMEC| DIT4TraMAuthors: Dimitrios Tsitsokas; Anastasios Kouvelas; Nikolas Geroliminis;Dimitrios Tsitsokas; Anastasios Kouvelas; Nikolas Geroliminis;Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dynamic traffic characteristics. Max Pressure (MP) distributed control gained significant popularity due to its theoretically proven ability of throughput maximization under specific assumptions. However, its effectiveness is questionable in over-saturated conditions, while network-scale implementation is often practically limited due to high instrumentation cost, which increases proportionally to the number of controlled intersections. Perimeter control (PC) based on the concept of Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated control strategy that regulates exchange flows between homogeneously congested regions, with the objective of maximizing traffic system performance and prevent over-saturation. However, homogeneity assumption is hardly realistic under congested conditions, which can compromise PC effectiveness. In this paper, network-wide parallel application of PC and MP strategies embedded in a two-layer control framework is evaluated in a macroscopic simulation environment. With the aim of reducing implementation cost of network-wide MP without significant performance drop, we propose a critical node identification algorithm that is based on node traffic characteristics and assess partial MP deployment to the most critical nodes. An enhanced version of Store-and-forward dynamic traffic paradigm incorporating finite queues and spill-back consideration is used to test different configurations of the two-layer framework, as well as each layer individually, for a real large-scale network, in moderate and highly congested conditions. Results show that: (i) combination of MP and PC outperforms individual layer application in almost all cases for both demand scenarios tested; (ii) MP control in critical node sets formed by the proposed strategy leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under stochastic demand fluctuations of up to 20% of mean. Transportation Research Part C: Emerging Technologies, 152 ISSN:0968-090X
Research Collection arrow_drop_down Transportation Research Part C Emerging TechnologiesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationshttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.trc.2023.104128&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 5 citations 5 popularity Top 10% influence Average impulse Average Powered by BIP!more_vert Research Collection arrow_drop_down Transportation Research Part C Emerging TechnologiesArticle . 2023 . Peer-reviewedLicense: CC BYData sources: CrossrefInfoscience - EPFL scientific publicationsOther literature typeData sources: Infoscience - EPFL scientific publicationshttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 ItalyPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:UKRI | GaN Sense, EC | Hexa-XUKRI| GaN Sense ,EC| Hexa-XBin Han; Mohammad Asif Habibi; Bjoern Richerzhagen; Kim Schindhelm; Florian Zeiger; Fabrizio Lamberti; Filippo Gabriele Pratticò; Karthik Upadhya; Charalampos Korovesis; Ioannis-Prodromos Belikaidis; Panagiotis Demestichas; Siyu Yuan; Hans D. Schotten;handle: 11583/2982926
Having the Fifth Generation (5G) mobile communication system recently rolled out in many countries, the wireless community is now setting its eyes on the next era of Sixth Generation (6G). Inheriting from 5G its focus on industrial use cases, 6G is envisaged to become the infrastructural backbone of future intelligent industry. Especially, a combination of 6G and the emerging technologies of Digital Twins (DT) will give impetus to the next evolution of Industry 4.0 (I4.0) systems. This article provides a survey in the research area of 6G-empowered industrial DT system. With a novel vision of 6G industrial DT ecosystem, this survey discusses the ambitions and potential applications of industrial DT in the 6G era, identifying the emerging challenges as well as the key enabling technologies. The introduced ecosystem is supposed to bridge the gaps between humans, machines, and the data infrastructure, and therewith enable numerous novel application scenarios. Comment: Accepted for publication in IEEE Open Journal of Vehicular Technology
Publications Open Re... arrow_drop_down IEEE Open Journal of Vehicular TechnologyArticle . 2023 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Publications Open Re... arrow_drop_down IEEE Open Journal of Vehicular TechnologyArticle . 2023 . Peer-reviewedLicense: CC BY NC NDData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/ojvt.2023.3325382&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2023 NetherlandsPublisher:Elsevier BV Funded by:EC | SAFE-UPEC| SAFE-UPAuthors: Sánchez, Manuel Muñoz; Silvas, Emilia; Elfring, Jos; van de Molengraft, René;Sánchez, Manuel Muñoz; Silvas, Emilia; Elfring, Jos; van de Molengraft, René;Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic behavior when presented with previously unseen inputs. In this work, two environment-aware models (MotionCNN and MultiPath++) and two common baselines (Constant Velocity and an LSTM) are benchmarked for robustness against various perturbations that simulate functional insufficiencies observed during model deployment in a vehicle: unavailability of road information, late detections, and noise. Results show significant performance degradation under the presence of these perturbations, with errors increasing up to +1444.8\% in commonly used trajectory prediction evaluation metrics. Training the models with similar perturbations effectively reduces performance degradation, with error increases of up to +87.5\%. We argue that despite being an effective mitigation strategy, data augmentation through perturbations during training does not guarantee robustness towards unforeseen perturbations, since identification of all possible on-road complications is unfeasible. Furthermore, degrading the inputs sometimes leads to more accurate predictions, suggesting that the models are unable to learn the true relationships between the different elements in the data.
IFAC-PapersOnLine arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert IFAC-PapersOnLine arrow_drop_down https://doi.org/10.48550/arxiv...Article . 2023License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.ifacol.2023.10.1256&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2022 DenmarkPublisher:Elsevier BV Funded by:EC | INTERPRETER, EC | ERANet SmartGridPlusEC| INTERPRETER ,EC| ERANet SmartGridPlusMüller, Nils; Chevalier, Samuel; Heinrich, Carsten; Heussen, Kai; Ziras, Charalampos;The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading to network violations. DSOs require reliable and accurate low-voltage state estimation (LVSE) to improve awareness and mitigate such events. However, the influence of flexibility activations on LVSE has not been addressed yet. It remains unclear if flexibility-induced uncertainty can be reliably quantified to enable robust DSO decision-making. In this work, uncertainty quantification in LVSE is systematically investigated for multiple scenarios of input availability and flexibility utilization, using real data. For that purpose, a Bayesian neural network (BNN) is compared to quantile regression. Results show that frequent flexibility activations can significantly deteriorate LVSE performance, unless secondary substation measurements are available. Moreover, it is demonstrated that the BNN captures flexibility-induced voltage drops by dynamically extending the prediction interval during activation periods, and that it improves interpretability regarding the cause of uncertainty. Submitted to the 22nd Power Systems Computation Conference (PSCC 2022)
Electric Power Syste... arrow_drop_down Online Research Database In TechnologyArticle . 2022Data sources: Online Research Database In Technologyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!more_vert Electric Power Syste... arrow_drop_down Online Research Database In TechnologyArticle . 2022Data sources: Online Research Database In Technologyadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Other literature type , Article 2022 SpainPublisher:Institute of Electrical and Electronics Engineers (IEEE) Funded by:EC | BRAVEEC| BRAVEAuthors: Ruben Izquierdo Gonzalo; Carlota Salinas Maldonado; Javier Alonso Ruiz; Ignacio Parra Alonso; +2 AuthorsRuben Izquierdo Gonzalo; Carlota Salinas Maldonado; Javier Alonso Ruiz; Ignacio Parra Alonso; David Fernandez Llorca; Miguel Angel Sotelo;handle: 10017/59771
Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess the required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow the evaluation of safety with real behaviors for critical and edge cases nor the evaluation of the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this article, we present and analyze the results of physical tests on the proving grounds of several predictive systems in automated driving functions developed within the framework of the BRidging Gaps for the Adoption of Automated VEhicles (BRAVE) project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions. Comunidad de Madrid; Ministerio de Ciencia e Innovación; Unión Europea Programa Horizon2020
arXiv.org e-Print Ar... arrow_drop_down Biblioteca Digital de la Universidad de AlcaláOther literature type . 2022License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de AlcaláIEEE Intelligent Transportation Systems MagazineArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mits.2022.3170649&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down Biblioteca Digital de la Universidad de AlcaláOther literature type . 2022License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de AlcaláIEEE Intelligent Transportation Systems MagazineArticle . 2022 . Peer-reviewedLicense: IEEE CopyrightData sources: Crossrefhttps://doi.org/10.48550/arxiv...Article . 2022License: arXiv Non-Exclusive DistributionData sources: Dataciteadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/mits.2022.3170649&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Preprint 2022 ItalyPublisher:Elsevier BV Funded by:EC | AI4EU, EC | AI4MediaEC| AI4EU ,EC| AI4MediaCiampi, Luca; Gennaro, Claudio; Carrara, Fabio; Falchi, Fabrizio; Vairo, Claudio; Amato, Giuseppe;This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focuses on the analysis of single images, this paper proposes the use of multiple visual sources to monitor a wider parking area from different perspectives. The proposed multi-camera system is capable of automatically estimate the number of cars present in the entire parking lot directly on board the edge devices. It comprises an on-device deep learning-based detector that locates and counts the vehicles from the captured images and a decentralized geometric-based approach that can analyze the inter-camera shared areas and merge the data acquired by all the devices. We conduct the experimental evaluation on an extended version of the CNRPark-EXT dataset, a collection of images taken from the parking lot on the campus of the National Research Council (CNR) in Pisa, Italy. We show that our system is robust and takes advantage of the redundant information deriving from the different cameras, improving the overall performance without requiring any extra geometrical information of the monitored scene. Submitted to Expert Systems With Applications
ISTI Open Portal arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2022.117929&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 8 citations 8 popularity Top 10% influence Average impulse Top 10% Powered by BIP!visibility 23visibility views 23 download downloads 29 Powered bymore_vert ISTI Open Portal arrow_drop_down add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.eswa.2022.117929&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Preprint , Article , Conference object 2022Publisher:IEEE Publicly fundedFunded by:EC | IoTCrawler, EC | ACTIVAGEEC| IoTCrawler ,EC| ACTIVAGEAuthors: Agarwal, Rachit; Elsaleh, Tarek; Tragos, Elias;Agarwal, Rachit; Elsaleh, Tarek; Tragos, Elias;Testing and experimentation are crucial for promoting innovation and building systems that can evolve to meet high levels of service quality. IoT data that belong to users and from which their personal information can be inferred are frequently shared in the background of IoT systems with third parties for experimentation and building quality services. This data sharing raises privacy concerns especially since in most cases the data are gathered and shared without the user's knowledge or explicit consent or for different purposes than the one for which the data were initially gathered. With the introduction of GDPR, IoT systems and experimentation platforms that federate data from different deployments, testbeds and data providers must be privacy-preserving. The wide adoption of IoT applications in scenarios ranging from smart cities to Industry 4.0 has raised concerns with respect to the privacy of users' data collected using IoT devices. Many experimental smart city applications are also using crowdsourcing data. Inspired by the GDPR requirements, we propose an IoT ontology built using available standards that enhances privacy, enables semantic interoperability between IoT deployments and supports the development of privacy-preserving experimental IoT applications. On top, we propose recommendations on how to efficiently use the ontology within IoT testbed and federating platforms. Our ontology is validated for different quality assessment criteria using standard validation tools. We focus on "experimentation" without loss of generality, because it covers scenarios from both research and industry, that are directly linked with innovation.
arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.1109/wf-iot...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/wf-iot54382.2022.10152206&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert arXiv.org e-Print Ar... arrow_drop_down https://doi.org/10.1109/wf-iot...Conference object . 2022 . Peer-reviewedLicense: STM Policy #29Data sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1109/wf-iot54382.2022.10152206&type=result"></script>'); --> </script>
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