Abstract
Understanding the behavioral intentions and decision-making mechanisms of automated vehicles (AVs) is one of the reasons why people trust them. This study aims to explore the main effect of AV transparency on trust, situation awareness and mental workload, while considering whether different driving experiences and scenario types have a moderating effect on the above results. This study conducted a driving simulation experiment. The experiment adopted a three-factor mixed design with AV transparency (No Explanation, What-only, Why-only, What+Why) and driving experience (Not Rich, Rich) as between-subject variables, and scenario type (Expected, Unexpected) as within-subject variables. To balance the impact of the scenario presentation order in the experiment, half of the participants were exposed to the scenarios in order from expected to unexpected, and the other half were exposed to the scenarios in order from unexpected to expected. Froty-eight participants (24 females and 24 males) participated in the experiment. The research found that increased transparency information help people better perceive and understand AVs while having less workload. In addition, the research found that drivers with rich driving experience are less dependent on the information provided by the AVs. The study also found that drivers have higher trust and lower anxiety in AVs in expected scenarios.
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Notes
- 1.
Expected events referred to events in which the drivers can understand the information provided by AV based on the current perception of the surrounding environment; unexpected events referred to events in which the drivers cannot understand the information provided by AV based on the current perception of the surrounding environment; takeover events referred to events in which control of the vehicle had to be handed over to the driver due to the malfunction in AV.
- 2.
In SA1 and SA2, the correct answer was underlined.
- 3.
***p < .001, **p < .01, *p < .05.
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Acknowledgments
This work was supported by the Guangdong Basic and Applied Basic Research Foundation (grant number 2021A1515011610) and the Foundation of Shenzhen Science and Technology Innovation Committee (grant number JCYJ20210324100014040).
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Huang, W., Chen, M., Li, W., Zhang, T. (2024). Effects of Automated Vehicles’ Transparency on Trust, Situation Awareness, and Mental Workload. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2024. Lecture Notes in Computer Science, vol 14732. Springer, Cham. https://doi.org/10.1007/978-3-031-60477-5_9
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