Exploring the Role of Predictability in Fostering Passenger Trust in Autonomous Ride-Hailing: A Case Study of Apollo Go

Abstract: The integration of autonomous vehicles (AVs) into everyday life presents a significant challenge: fostering user trust. It is crucial to foster passenger trust in order to facilitate the acceptance and continued use of autonomous vehicles (AVs), particularly in the context of ride-hailing services such as Apollo Go. Users may be reluctant to utilise self-driving internet rides due to the absence of human supervision. Trust can be conceptualised as a belief in the safety, reliability, and predictability of self-driving cars. Among these attributes, predictability is of particular importance. Passengers are more likely to trust systems that are able to predict and understand their behaviour. This study examines the role of predictability in enhancing passenger trust in self-driving ride-hailing services, with Apollo Go serving as a case study.This study employs a methodology that integrates quantitative modelling with qualitative user studies. Quantitative data were gathered through controlled simulated driving. In a simulated driving environment, passengers experienced varying degrees of transparency in vehicle behaviour and decision-making processes. Qualitative data were collected through in-depth interviews and surveys to assess passenger trust in the predictability and transparency of self-driving cars. The core of the research methodology is to assess passengers' trust in vehicle behaviour, decision-making, and future actions under different levels of transparency.This study categorises transparency into three levels: basic behavioural transparency (e.g. real-time operations such as speed, braking, and cornering), situational decision-making transparency (understanding the rationale behind the decision), and predictive transparency (predicting the future behaviour of the vehicle). This layered model ensures that users have access to the appropriate level of information at the appropriate time, thus facilitating an intuitive understanding of the decision-making process for self-driving cars.The findings indicate that providing a transparent representation of the vehicle's behaviour and a predictable basis for decision-making, as well as future vehicle operations, can significantly enhance user perceptions of the safety and reliability of self-driving car systems. In particular, integrating predictive transparency increases passenger confidence in the vehicle's ability to handle complex situations, such as sudden stops or unforeseen roadway events. Additionally, the study demonstrated that higher perceived transparency was associated with lower passenger anxiety and higher levels of comfort.This study makes a contribution to the field by developing a framework that enhances passenger trust in self-driving ride-hailing services. It identifies the types of transparency that influence trust and provides insights for designing user-centred interfaces for self-driving cars. The findings emphasise the importance of predictability in improving user experience and trust, offering guidance for interface and system design for autonomous ride-hailing services.It should be noted that this study is not without limitations. Firstly, the study was conducted in a specific geographic area, which may limit the generalisability of the results. Secondly, the study focused primarily on transparency in driving behaviour and decision-making, and did not delve into other factors that influence trust, such as safety features or prior autonomous driving experiences.As autonomous vehicles become an integral part of the global transportation system, it is of the utmost importance to foster user awareness and trust. Transparent and predictable interaction mechanisms not only accelerate adoption but also enhance security, satisfaction, and long-term availability. This research presents practical insights for designing autonomous vehicle systems that enhance user confidence through a case study of Apollo Go. The findings emphasise the significance of transparent communication regarding vehicle behaviour and the assurance of seamless, predictable driving in order to promote passenger safety. Further research could extend these findings by exploring the long-term effects of trust-oriented design and examining how ongoing interactions affect user behaviour and acceptance. Additionally, incorporating cultural and demographic factors into personalised design strategies could enhance global adoption rates and meet the needs of diverse users.

Keywords: Autonomous Vehicles, AVs, User Trust, Transparency, Predictive Transparency, Ride-Hailing Services

DOI: 10.54941/ahfe1006527

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