This article reports on a study to investigate how the driving behaviour of autonomous vehicles influences trust and acceptance. Two different designs were presented to two groups of participants (n = 22/21), using actual autonomously driving vehicles. The first was a vehicle programmed to drive similarly to a human, “peeking” when approaching road junctions as if it was looking before proceeding. The second design had a vehicle programmed to convey the impression that it was communicating with other vehicles and infrastructure and “knew” if the junction was clear so could proceed without ever stopping or slowing down. Results showed non-significant differences in trust between the two vehicle behaviours. However, there were significant increases in trust scores overall for both designs as the trials progressed. Post-interaction interviews indicated that there were pros and cons for both driving styles, and participants suggested which aspects of the driving styles could be improved. This paper presents user information recommendations for the design and programming of driving systems for autonomous vehicles, with the aim of improving their users’ trust and acceptance.

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While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during 'steady state' operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles. INDEX TERMS Intelligent vehicles, autonomous vehicles, interface, eye tracking, information requirements, HMI.

App stores allow globally distributed users to submit user feedback, in the form of user reviews, about the apps they download. Previous research has found that many of these reviews contain valuable information for software evolution, such as bug reports or feature requests, and has designed approaches for automatically extracting this information. However, the diversity of the feedback submitted by users from diverse cultural backgrounds and the consequences this diversity might imply have not been studied so far. In this paper, we report on a cross-cultural study where we investigated cultural differences in app store reviews and identified correlations to cultural dimensions taken from a well-established cultural model. We analyzed 2,560 app reviews written by users from eight countries with diverse national culture. We contribute evidence about the influence of cultural factors on characteristics of app reviews. Our results also help developers of automated feedback analysis tools to avoid cultural bias when choosing their algorithms and the data for training and validating them. ABSTRACTApp stores allow globally distributed users to submit user feedback, in the form of user reviews, about the apps they download. Previous research has found that many of these reviews contain valuable information for software evolution, such as bug reports or feature requests, and has designed approaches for automatically extracting this information. However, the diversity of the feedback submitted by users from diverse cultural backgrounds and the consequences this diversity might imply have not been studied so far.In this paper, we report on a cross-cultural study where we investigated cultural differences in app store reviews and identified correlations to cultural dimensions taken from a well-established cultural model. We analyzed 2,560 app reviews written by users from eight countries with diverse national culture. We contribute evidence about the influence of cultural factors on characteristics of app reviews. Our results also help developers of automated feedback analysis tools to avoid cultural bias when choosing their algorithms and the data for training and validating them.