With automated driving being on the cusp of wide spread market introduction (Rivera & van der Meulen, 2014), Human Factors aspect of design and evaluation of the exact behaviour of automated vehicles gains crucial importance. Safety margins determine an envelope of possible trajectories for the automated vehicle, but as of today, the parameterisation of the vehicle’s behaviour within that envelope to create attractive driving styles has has not received wide spread attention yet (Scherer et al., 2015). However, for acceptance of automated driving functions, deemed critical to deliver the promised reduction in accident numbers, it is essential to design automated driving styles that win end users over to activate them on public roads. In two consecutive studies we investigated how to measure preferences for automated driving styles, whether or not drivers prefer being driven similar to their own driving, or if at least default styles can be created that a majority of the users enjoy. In a first study 43 subjects drove in three scenarios on a two-lane motorway in a motion-based driving simulator. Users were instructed to drive at 120 km/h, forcing them to overtake slower vehicles on the right lane. The scenarios were varied regarding the presence and behaviour of faster cars on the left lane, compelling subjects to decide on timing and execution of overtaking manoeuvres. Based on the data from this study, four prototypical driving styles were extracted, using a multivariate time-series clustering algorithm (Griesche et al., 2014). In a second study, 35 subjects from study 1 rated the attractiveness of the prototypical driving styles gained from that study, in addition to their own style. Using a Best-Worst-Scaling technique, preferences for either one of the prototypical driving styles or their own style could be measured. The results indicate that i) many but not all subjects do like their own styles, but not in every situation, and ii) certain styles exist which are preferred by the great majority of the users.


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    Titel :

    Should my vehicle drive as I do? A methodology to determine drivers‘ preference for automated driving styles.


    Beteiligte:

    Kongress:

    2017 ; Dresden


    Erscheinungsdatum :

    2017-03-01


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch