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In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

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Different levels of automation require distinct tasks of the driver. During partially automated driving, the driver has to supervise the system and the environment, whereas the driver can engage in non-driving related tasks during highly automated driving. With more aspects of the driving task being automated, the driver’s role becomes more passive. In the future, automated driving systems may combine several levels of automation. Thus, the driver’s tasks and responsibilities vary in a multi-level automated driving vehicle. To support drivers in their tasks, the automated vehicle should provide comprehensible and perceptible feedback. In addition, automated driving systems should provide information about the system’s state, its intentions and transitions transparently to assist the drivers´ mode awareness. Prior research has mainly focused on information that is presented visually. However, previous studies found that vestibular feedback also has a positive effect on mode awareness. Thus, based on multiple resource theory, we examined the effect of different feedback strategies including vestibular feedback on mode awareness and system comprehension in the context of automated driving.A real driving study was conducted on a German highway, covering approximately 130 km per participant. Therefore, a prototype was used, which performed lateral and longitudinal vehicle guidance. In a between-design, a total of N = 36 participants were randomly assigned to one of two feedback strategies. Both strategies consisted of visual-auditory feedback, with one concept including additional active vehicle motions in partially automated driving. The vestibular feedback consisted of pitch and roll motions. Pitch motions indicated a detected preceding vehicle, whereas roll motions announced lane changes. Participants experienced manual, partially and highly automated driving as well as transitions between these levels. In two thirds of the cases the automated vehicle decelerated slowly at the end of the test drive, simulating a system failure. Finally, trust, acceptance and mode awareness were collected through several questionnaires.Statistical analysis revealed that the strategy with vestibular feedback in partially automated driving generated significantly more trust and was perceived more reliable. Although no significant differences between the strategies were found for acceptability, both were assessed as highly acceptable and acceptance ratings increased over time. Concerning mode awareness, no group differences were found for function awareness and monitoring behavior. However, a significant effect indicated that additional active vehicle motions were perceived as more supportive in complex situations. Further results exhibited that regardless of the strategy, highly automated driving led to significantly more function awareness than partially automated driving.In summary, these results show that the feedback strategy with different designs for partially and highly automated driving can increase the reliability of the automated vehicle. Consistent with the literature, results also indicate that a feedback strategy with additional active vehicle motions in partially automated driving increases trust in automation. Thus, this study suggests that different designs for partially and highly automated driving, including additional vestibular feedback, opens upa new possibility to support drivers in their tasks.