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  • 1.
    Axelsson, Jakob
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Safety in Vehicle Platooning: A Systematic Literature Review2017In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 18, no 5, p. 1033-1045Article, review/survey (Refereed)
    Abstract [en]

    Vehicle platooning has been studied for several decades, with objectives such as improved traffic throughput on existing infrastructure or reduced energy consumption. All the time, it has been apparent that safety is an important issue. However, there are no comprehensive analyses of what is needed to achieve safety in platooning, but only scattered pieces of information. This paper investigates, through a systematic literature review, what is known about safety for platooning, including what analysis methods have been used, what hazards and failures have been identified, and solution elements that have been proposed to improve safety. Based on this, a gap analysis is performed to identify outstanding questions that need to be addressed in future research. These include dealing with a business ecosystem of actors that cooperate and compete around platooning, refining safety analysis methods to make them suitable for systems-of-systems, dealing with variability in vehicles, and finding solutions to various human factors issues.

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  • 2.
    Besinovic, Nikola
    et al.
    Delft Univ Technol, Dept Transport & Planning, NL-2600 GA Delft, Netherlands..
    De Donato, Lorenzo
    Univ Naples Federico II, Dept Elect Engn & Informat Technolo2y, I-80125 Naples, Italy..
    Flammini, Francesco
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Goverde, Rob M. P.
    Delft Univ Technol, Dept Transport & Planning, NL-2600 GA Delft, Netherlands..
    Lin, Zhiyuan
    Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, England..
    Liu, Ronghui
    Univ Leeds, Inst Transport Studies, Leeds LS2 9JT, W Yorkshire, England..
    Marrone, Stefano
    Univ Naples Federico II, Dept Elect Engn & Informat Technolo2y, I-80125 Naples, Italy..
    Nardone, Roberto
    Univ Naples Parthenope, Dept Engn, I-80143 Naples, Italy..
    Tang, Tianli
    Southeast Univ, Sch Transportat, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Jiangsu Key Lab Urban ITS, Nanjing 211189, Peoples R China..
    Vittorini, Valeria
    Univ Naples Federico II, Dept Elect Engn & Informat Technolo2y, I-80125 Naples, Italy..
    Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications2022In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 9, p. 14011-14024Article in journal (Refereed)
    Abstract [en]

    Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

  • 3.
    Meo, Carlo Di
    et al.
    University of Naples Federico II, Italy.
    Vaio, Marco Di
    University of Naples Federico II, Italy.
    Flammini, Francesco
    Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM).
    Nardone, Roberto
    University of Naples Federico II, Italy.
    Santini, Sefania
    University of Naples Federico II, Italy.
    Vittorini, Valeria
    University of Naples Federico II, Italy.
    ERTMS/ETCS Virtual Coupling: Proof of Concept and Numerical Analysis2019In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 21, no 6, p. 2545-2556Article in journal (Refereed)
    Abstract [en]

    Railway infrastructure operators need to push their network capacity up to their limits in high-traffic corridors. Virtual coupling is considered among the most relevant innovations to be studied within the European Horizon 2020 Shift2Rail Joint Undertaking as it can drastically reduce headways and thus increase the line capacity by allowing to dynamically connect two or more trains in a single convoy. This paper provides a proof of concept of Virtual coupling by introducing a specific operating mode within the European rail traffic management system/European train control system (ERTMS/ETCS) standard specification, and by defining a coupling control algorithm accounting for time-varying delays affecting the communication links. To that aim, we define one ploy to enrich the ERTMS/ETCS with Virtual coupling without changing its working principles and we borrow a numerical analysis methodology used to study platooning in the automotive field. The numerical analysis is also provided to support the proof of concept with quantitative results in a case-study simulation scenario.

  • 4.
    Shi, Xiaodan
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778568, Japan..
    Zhang, Haoran
    Peking Univ, Sch Urban Planning & Design, Shenzhen 518055, Guangdong, Peoples R China..
    Yuan, Wei
    Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778568, Japan..
    Shibasaki, Ryosuke
    Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778568, Japan..
    MetaTraj: Meta-Learning for Cross-Scene Cross-Object Trajectory Prediction2023In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Article in journal (Refereed)
    Abstract [en]

    Long-term pedestrian trajectory prediction in crowds is highly valuable for safety driving and social robot navigation. The recent research of trajectory prediction usually focuses on solving the problems of modeling social interactions, physical constraints and multi-modality of futures without considering the generalization of prediction models to other scenes and objects, which is critical for real-world applications. In this paper, we propose a general framework that makes trajectory prediction models able to transfer well across unseen scenes and objects by quickly learning the prior information of trajectories. The trajectory sequences are closely related to the circumstance setting (e.g. exits, roads, buildings, entries etc.) and the objects (e.g. pedestrians, bicycles, vehicles etc.). We argue that those trajectory information varying across scenes and objects makes a trained prediction model not perform well over unseen target data. To address it, we introduce MetaTraj that contains carefully designed sub-tasks and meta-tasks to learn prior information of trajectories related to scenes and objects, which then contributes to accurate long-term future prediction. Both sub-tasks and meta-tasks are generated from trajectory sequences effortlessly and can be easily integrated into many prediction models. Extensive experiments over several trajectory prediction benchmarks demonstrate that MetaTraj can be applied to multiple prediction models and enables them generalize well to unseen scenes and objects.

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