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International Conference on Internet of Vehicles

IOV 2018: Internet of Vehicles. Technologies and Services Towards Smart City pp 133–145Cite as

Social Knowledge to Improve Situation Awareness of Assistance Systems in City Driving

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11253))

Abstract

City traffic is getting more multi-modal, with a variety of actors and mobility options in mixed spaces. This makes decisions on traffic behaviour and control more complex. Beyond traditionally considered aspects (e.g. traffic state or used vehicle), human aspects (e.g. physical state, displacement goal, or companion), gain increasing relevance. They can greatly modify how people move and interact with others. Introducing social knowledge about human behaviour and context can help to better understand and anticipate the environment and its actions. This paper proposes the development of Social-Aware Driver Assistance Systems (SADASs) for that purpose. A SADAS uses traffic social properties that formalize social knowledge using a template organized around diagrams. The diagrams are compliant with a specific modelling language, which is intended to describe social aspects in a given context. They facilitate the integration of this knowledge with system specifications, and its semi-automated verification both in design and run time. A case study on a distributed obstacle detection system for vehicles extended with social knowledge to anticipate people’ behaviour illustrates the approach.

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Acknowledgment

This work has been done in the context of the projects “RISE Women with disabilities In Social Engagement (RISEWISE)” (grant 690874) supported by the European Commission in the Horizon 2020 programme,“Collaborative Ambient Assisted Living Design (ColoSAAL)” (grant TIN2014-57028-R) and “Research Thematic Network on Smart Cities” (grant TIN2016-81766-REDT) supported by the Spanish Ministry for Economy, Industry, and Competitiveness, MOSI-AGIL-CM (grant S2013/ICE-3019) supported by the Autonomous Region of Madrid and co-funded by EU Structural Funds FSE and FEDER, and the“Programa de Creación y Consolidación de Grupos de Investigación” (UCM-BSCH GR35/10-A).

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Authors and Affiliations

  1. Universidad Rey Juan Carlos, Móstoles, Spain

    Alberto Fernández-Isabel

  2. Research Group on Agent-based, Social & Interdisciplinary Applications (GRASIA), Universidad Complutense de Madrid, Madrid, Spain

    Rubén Fuentes-Fernández

Authors
  1. Alberto Fernández-Isabel
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  2. Rubén Fuentes-Fernández
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Corresponding author

Correspondence to Rubén Fuentes-Fernández .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Andrzej M.J. Skulimowski

  2. University of Sussex, Brighton, UK

    Zhengguo Sheng

  3. Université de Versailles St Quentin, Versailles, France

    Sondès Khemiri-Kallel

  4. Université Paris 13, Villetaneuse, France

    Christophe Cérin

  5. National Chung Cheng University, Minxiong, Taiwan

    Ching-Hsien Hsu

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Fernández-Isabel, A., Fuentes-Fernández, R. (2018). Social Knowledge to Improve Situation Awareness of Assistance Systems in City Driving. In: Skulimowski, A., Sheng, Z., Khemiri-Kallel, S., Cérin, C., Hsu, CH. (eds) Internet of Vehicles. Technologies and Services Towards Smart City. IOV 2018. Lecture Notes in Computer Science(), vol 11253. Springer, Cham. https://doi.org/10.1007/978-3-030-05081-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-05081-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05080-1

  • Online ISBN: 978-3-030-05081-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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