Automated vehicles (AVs, level 4) are coming on the market in forthcoming years, but it is unsure what is the operational capability of these vehicles in the first place. Especially in four-season countries, winter conditions, such as snowfall and minor amount of daylight hours, increase the operational requirements of AVs. In this study, the potential impacts of AVs on pedestrian safety are evaluated in Finland. The study evaluates which in-depth investigated 40 fatal crashes between pedestrians and driver-managed cars could have been avoided in the best possible situation had the cars been replaced by the AVs. The maximum safety impacts are evaluated in three scenarios. The scenarios are based on the AV’s potentially varied abilities to operate in snowy and low-light conditions, and without lane markings. In addition, a time-to-collision analysis was made for each crash. According to the analysis, 28-73% of the pedestrian crashes are avoidable in the different scenarios. In the basic scenario, in which the operational capability of AV (level 4) is limited, solely 28% of the crashes are avoidable. In a full automation scenario (level 5), 73% of the crashes are potentially avoidable. In reality, some drivers would probably like to drive manually instead of automated driving, which would likely reduce the potential safety impacts. This hypothetical scenario is analyzed and discussed. In the countries with varied weather and road conditions, the operational requirements of the AVs are demanding. In order to ensure the maximum safety potential of the AVs in different countries, the AVs should be able to operate in various conditions and environments.


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    Titel :

    The potential impacts of automated vehicles on pedestrian safety in a four-season country


    Beteiligte:
    Utriainen, R. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2020-11-11


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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